# A review of AutoML optimization techniques for medical image applications

Muhammad Junaid Ali, Mokhtar Essaid, Laurent Moalic, Lhassane Idoumghar

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Muhammad Junaid Ali, Mokhtar Essaid, Laurent Moalic, Lhassane Idoumghar. A review of AutoML optimization techniques for medical image applications. Computerized Medical Imaging and Graphics, 2024, 118, pp.102441. 10.1016/j.compmedimag.2024.102441. hal-04976935

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Graphical Abstract

A Review of AutoML Optimization Techniques for Medical Image Applications

Muhammad Junaid Ali,Mokhtar Essaid,Laurent Moalic,Lhassane Idoumghar

<figure><img src="image_2.png" /></figure>
Highlights

A Review of AutoML Optimization Techniques for Medical Image Applications

Muhammad Junaid Ali,Mokhtar Essaid,Laurent Moalic,Lhassane Idoumghar

* This paper discusses the background and importance of Automated Machine Learning techniques for various medical imaging tasks.

* This study explores the research trends and future directions in the adaptation of AutoML techniques across different medical imaging domains.
# A Review of AutoML Optimization Techniques for Medical Image Applications

Muhammad Junaid Ali<sup>a,**</sup>, Mokhtar Essaid<sup>a</sup>, Laurent Moalic<sup>a</sup> and Lhassane Idoumghar<sup>a</sup>

<sup>a</sup>Université de Haute-Alsace, IRIMAS UR7499, Mulhouse, 68100, France

## ARTICLE INFO

Keywords:

Automated deep learning
Automated Machine Learning
Medical imaging
Neural architecture search
Automated Data Augmentation

## ABSTRACT

Automatic analysis of medical images using machine learning techniques has gained significant importance over the years. A large number of approaches have been proposed for solving different medical image analysis tasks using machine learning and deep learning approaches. These approaches are quite effective thanks to their ability to analyze large volume of medical imaging data. Moreover, they can also identify patterns that may be difficult for human experts to detect. Manually designing and tuning the parameters of these algorithms is a challenging and time-consuming task. Furthermore, designing a generalized model that can handle different imaging modalities is difficult, as each modality has specific characteristics. To solve these problems and automate the whole pipeline of different medical image analysis tasks, numerous Automatic Machine Learning (AutoML) techniques have been proposed. These techniques include Hyper-parameter Optimization (HPO), Neural Architecture Search (NAS), and Automatic Data Augmentation (ADA). This study provides an overview of several AutoML-based approaches for different medical imaging tasks in terms of optimization search strategies. The usage of optimization techniques (evolutionary, gradient-based, Bayesian optimization, etc.) is of significant importance for these AutoML approaches. We comprehensively reviewed existing AutoML approaches, categorized them, and performed a detailed analysis of different proposed approaches. Furthermore, current challenges and possible future research directions are also discussed.

## 1. Introduction

Machine Learning (ML) algorithms have been widely used for the last two decades to solve different medical image analysis tasks. The rise of deep learning enables its application in different fields, especially for various medical imaging tasks. Although these architectures and approaches are effective, one of the main problems with these approaches is their heavy reliance on prior knowledge and the experience of researchers. Designing these architectures and tuning their parameters is a time-consuming and computationally intensive task due to the multiple experiments required to evaluate every combination of parameters and the architecture.

Automated Machine Learning (AutoML) has been recently proposed to automate traditional machine learning tasks. It assists in automatically tuning and designing deep learning architectures instead of relying on human experts and reducing the required effort [154]. AutoML automates different steps of traditional ML algorithms, such as preprocessing, feature engineering, model selection, and Hyper-Parameter Optimization (HPO) [78]. Automated designing of deep learning architectures and their parameters tuning is one of the essential tasks in AutoML. The design of deep learning architectures, also called Neural Architecture Search (NAS) has the potential to surpass the human design

of deep neural networks for both visual recognition and natural language processing tasks.

Apart from NAS, tuning hyper-parameters and automatic model selection are also important tasks. The HPO technique automatically searches for the best configuration of a given architecture, and model selection chooses the best set of models. Moreover, the problem of limited data availability in medical image analysis is overcome by automatically searching for a data augmentation policy, also called ADA. Choosing the optimal data augmentation policy for a given dataset is a difficult task and there is no single policy which works well on all datasets. ADA overcomes this problem by searching for a policy for given datasets. Furthermore, the deep learning models designed for multiple medical image analysis tasks are large in size and consist of large numbers of parameters. Automatic model compression-based and model pruning techniques reduce the model parameters, making it more robust against predictions.

Following this context, multiple AutoML approaches have been proposed for solving different medical image tasks, e.g., classification, segmentation, reconstruction, and denoising. Medical imaging is a technique that creates visual representations of the interior body for clinical analysis and medical interventions. It is crucial in diagnosing, monitoring, and treating various medical conditions. Medical image analysis uses computer algorithms and techniques to automatically analyze these medical images. These approaches extract relevant information from medical images to assist in treatment planning, diagnosis, survival analysis, and disease monitoring. Recently, it has been shown that neural network

<figure><img src="image_3.png" /></figure>

<footnote>** Corresponding author</footnote>

<footnote>muhammad-junaid.ali@uha.fr (M.J. Ali); mokhtar.essaid@uha.fr (M. Essaid); laurent.moalic@uha.fr (L. Moalic); lhassane.idoumghar@uha.fr (L. Idoumghar)</footnote>

<footnote>ORCID(s):
1</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 1:** An overview of categorization of the different approaches proposed for AutoML in Medical Imaging</figcaption></figure>

architectures are essential in building machine learning-based systems to solve different medical image analysis problems. Over the years, numerous architectures have been proposed to solve these tasks. However, these architectures are built after multiple attempts to combine different types of convolution and pooling layers requiring important resources such as testing time and extensive human expertise. As an attempt to overcome this issue, AutoML-based NAS algorithms are effective in this case, as they can automatically search for an architecture for a given task.

The significant advantage of using AutoML approaches over traditional ML approaches for medical imaging tasks is the ability of AutoML techniques to systematically explore hyper-parameter and architectural search spaces. It identifies optimal configurations for applying ML models to medical images. These AutoML approaches are guided by a certain number of optimization strategies, which are an essential block of these AutoML systems. They use prior knowledge of learning behavior to find the optimal solution for a given problem. These optimization strategies embedded within AutoML approaches (NAS, HPO, ADA, etc.) find SOTA solutions to optimize these tasks.

Recently, AutoML-based solutions have been introduced for medical imaging tasks, especially for medical image segmentation, such as nnUNET and Auto3DSeg, which search for optimal parameters and automatically configure the pipeline for performing medical imaging tasks [79] [121] [130]. Besides, multiple AutoML NAS algorithms have been proposed to search for an architectural topology [74] [129]. These approaches have won different medical image

challenges, demonstrating their effectiveness over traditional ML approaches. Also, there are multiple ADA approaches proposed for medical imaging tasks. Existing AutoML approaches for medical images are time-consuming as medical imaging data consists of a large number of samples, and evaluating individual configurations is computationally expensive. Consequently, multiple performance estimation strategies have been proposed to reduce the required searching time.

Furthermore, AutoML tools also enable healthcare workers to develop machine-learning models without prior knowledge of ML. Mainly, AutoML approaches are focused on different tasks, including ADA, Hyper-Parameters Optimization (HPO), NAS, and automatic pruning of deep learning architectures. The high-level categorization of the different proposed approaches for AutoML in medical imaging is shown in Figure 1.

Moreover, the advantages of adopting AutoML approaches over classical ML approaches to solve these tasks are visually demonstrated in Figure 2. Based on these advantages, the key motivations regarding the adaptation of AutoML to solve these tasks are as follows:

* As compared to natural images, medical images are complex due to variations and domain shift caused by data characteristics of media centers and scanners.

* Medical datasets often consist of limited number of samples and exhibit class-imbalance problem which
<figure><img src="image_5.png" /><figcaption>**Figure 2:** Overview of advantages of SOTA AutoML approaches over classical approaches for medical image analysis tasks</figcaption></figure>

can be solved by selective data-augmentation techniques. However, choosing an optimal data augmentation policy for specific problem is challenging. To overcome this problem, numerous data augmentation techniques have been proposed which utilizes different optimization algorithms and there is a need for an efficient data augmentation technique specifically for medical images.

* Prediction time of deep learning architectures designed for medical image applications is crucial. Therefore, building lightweight hardware-aware architectures consisting of less parameters and Floating

Point Operations (FLOPS is necessary. NAS automatically finds lightweight architecture for a given task, whereas HW-NAS focuses on finding lightweight architectures having less inference time.

* AutoML NAS and HPO approaches can effectively reduce the cost and the time required to manually tune, analyse medical images and design architectures leading to more efficient and cost-effective healthcare services.

Due to the importance of AutoML for solving medical image analysis tasks. A detailed comparative study is necessary to
<table><caption>**Table 1**</caption><thead><tr><th></th><th>[127]</th><th>[126]</th><th>[173]</th><th>[215]</th><th>[166]</th><th>[19]</th><th>[106]</th><th>[47]</th><th>[138]</th><th>[16]</th><th>Ours</th></tr></thead><tbody><tr><td>Neural Architecture Search</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Hyper-parameter Optimization</td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Automatic Data Augmentation</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Combined HPO+NAS</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Analysis of AutoML tools</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Overview of AutoML in Medical Image Analysis</td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr></tbody></table>

Comparison of this study with existing studies with respect to different AutoML techniques

<table><caption>**Table 2**</caption><thead><tr><th>Inclusion Criteria</th><th>Exclusion Criteria</th></tr></thead><tbody><tr><td>Articles which are performing multiple medical imaging tasks automatically by utilizing AutoML techniques</td><td>Articles which are not using AutoML techniques to perform medical imaging tasks</td></tr><tr><td>Papers with proper evaluation metrics and detailed summary of the proposed approach including comparison with manual approaches</td><td>Articles which are not peer-reviewed by some conference or journals</td></tr><tr><td>Articles which cover diverse range of medical imaging applications</td><td>Articles that don't pertain to medical image analysis although they used AutoML approaches</td></tr></tbody></table>

Inclusion and exclusion criteria for papers selection adopted in this study

highlight the current progress of AutoML for medical image applications, the associated challenges and the possible research directions. The main contributions of our study are:

* We investigate the necessity, types and challenges associated with AutoML approaches proposed for medical image applications.

* We discuss the problems solved by the current approaches and categorize them for better understanding.

* We have categorized the AutoML approaches with respect to optimization strategies for solving different medical imaging tasks.

* We have provided some of the future directions for research in the area of AutoML for medical image applications.

## 1.1. Comparison of existing studies

The main focus of our study is to provide a detailed overview of different studies adopting AutoML steps (HPO, ADA, and NAS) for medical image applications. There are numerous existing studies that discuss the potential applications of AutoML approaches in different domains [73], [86], [214]. Although this is not the first study to discuss the potential applications of AutoML for medical image applications, and several studies have been published in the literature, previous studies have only discussed abstract concepts and have not explored in depth how AutoML could be applied for medical image analysis tasks.

To the best of our knowledge, only three studies discuss different applications of NAS for medical image applications. In [166], the authors overviewed NAS for medical

image applications. However, their only focus was on NAS-based AutoML applications. Other AutoML approaches should have been discussed, such as HPO and ADA. Furthermore, in [138], the authors provide a detailed overview of NAS approaches for medical image segmentation, where NAS approaches at the network and cell levels were studied. In [16], the authors explored different search spaces designed for medical tasks and discussed different NAS approaches proposed for solving different medical tasks. Their primary focus was to explore different efficient NAS approaches for medical tasks.

Indeed, several existing studies performed an overview of different propositions introduced for specific AutoML tasks, such as HPO, and designing deep learning architectures, such as Mohakud et al. The latter work conducted a detailed survey on different HPO techniques using nature-inspired algorithms [118]. Similarly, in [4], a comprehensive study on optimizing deep learning architectures using nature-inspired algorithms is performed. Moreover, several surveys were conducted on AutoML for healthcare. However, their focus was on healthcare data rather than medical image analysis.

Furthermore, in [173], the author gave a general overview of AutoML, its approaches in healthcare, and how it is helpful in different healthcare applications. Following the same context, Mustafa et al. summarized the use of AutoML in the healthcare domain specifically for healthcare notes analysis and medical notes processing [120]. Table 1 summarizes the comparison of our survey with existing studies.

## 1.2. Organization of Survey

The survey is organized based on different proposed approaches and further explained with respect to different optimization strategies. First, AutoML, its components, and its usage in medical imaging are discussed. Then, benchmark
<figure><img src="image_6.png" /><figcaption>**Figure 3:** The organization of the survey</figcaption></figure>

datasets used by AutoML studies are explored. After that, the current studies are analyzed in the analysis and discussion section, the possible future research directions are discussed, and then the study is concluded. Figure 3 depicts the organization of the survey. The abbreviations used in this paper are given in Table 3.

## 2. Survey Methodology

This section describes the methodology adopted in this study to include articles and the statistics of selected articles concerning tools and techniques used. The idea is to visualize the adaptation trends of AutoML techniques in different medical image analysis tasks.

### 2.1. Papers Distribution

In this section, we present the distribution of the published articles in terms of AutoML tasks, medical imaging tasks, search strategies, and year-wise trends. This section provides an overview of the existing literature regarding tasks solved by different AutoML approaches and the numerous medical imaging tasks solved by these techniques. Table 2 gives this study's inclusion and exclusion criteria. At first, the articles are searched from different article repositories "IEEE Xplore", "Google Scholar", "Science Direct", "Springer", "Scopus", "" and "ResearchGate" containing different keywords "AutoML", "Automatic designing", "NAS", "Neural Architecture Search", "Architecture Search", "Automatic Data Augmentation", "Automatic Classification" etc.
<table><caption>**Table 3**</caption><thead><tr><td>Abbreviation</td><td>Description</td><td>Abbreviation</td><td>Description</td></tr></thead><tbody><tr><td>ASS</td><td>Automatic Subset Search</td><td>IP</td><td>Integer Programming</td></tr><tr><td>AP</td><td>Augmentation Policy</td><td>IDC</td><td>Invasive Ductal Carcinoma</td></tr><tr><td>ADA</td><td>Automatic Data Augmentation</td><td>IoT</td><td>Internet Of Things</td></tr><tr><td>AI</td><td>Artificial Intelligence</td><td>LLM</td><td>Large Language Models</td></tr><tr><td>AEA</td><td>Automatic Explainable AI</td><td>MB</td><td>MegaBytes</td></tr><tr><td>ANAO</td><td>Adaptive Neural Architecture Optimization</td><td>MOEA</td><td>Multi-Objective Evolutionary Algorithm</td></tr><tr><td>AutoML</td><td>Automatic Machine Learning</td><td>MOEA/D</td><td>Multi-Objective Evolutionary Algorithm with Decomposition</td></tr><tr><td>BTLO</td><td>Binary Teaching-Learning-Based Optimization</td><td>MRI</td><td>Medical Resonance Imaging</td></tr><tr><td>BF</td><td>Brute-Force</td><td>MRF</td><td>Markov Random Fields</td></tr><tr><td>BO</td><td>Bayesian Optimization</td><td>NN</td><td>Neural Network</td></tr><tr><td>CNN</td><td>Convolutional Neural Network</td><td>NAS</td><td>Neural Architecture Search</td></tr><tr><td>CT</td><td>Computed Tomography</td><td>ML</td><td>Machine Learning</td></tr><tr><td>CV</td><td>Computer Vision</td><td>OCT</td><td>Optical Coherence Tomography</td></tr><tr><td>CLPSO</td><td>Comprehensive Learning Particle Swarm Optimization</td><td>POP</td><td>Partial Order Pruning</td></tr><tr><td>CBAM</td><td>Convolution Block Attention Module</td><td>PRIL</td><td>Pretext-Invariant Representations</td></tr><tr><td>DARTS</td><td>Differentiable Architecture Search</td><td>PSO</td><td>Particle Swarm Optimization</td></tr><tr><td>DE</td><td>Differential Evolution</td><td>RAT</td><td>Regularized Adversarial Training</td></tr><tr><td>DSC</td><td>Dice Similarity Coefficient</td><td>RSA</td><td>Random Sampling Approach</td></tr><tr><td>DBN</td><td>Deep Belief Network</td><td>RBF</td><td>Radial Based Function</td></tr><tr><td>DL</td><td>Deep Learning</td><td>RF</td><td>Random Forest</td></tr><tr><td>DNN</td><td>Deep Neural Network</td><td>RL</td><td>Reinforcement Learning</td></tr><tr><td>EA</td><td>Evolutionary Algorithm</td><td>RNN</td><td>Recurrent Neural Network</td></tr><tr><td>FCN</td><td>Fully Convolutional Network</td><td>RPN</td><td>Region Proposal Network</td></tr><tr><td>FL</td><td>Federated Learning</td><td>RS</td><td>Random Search</td></tr><tr><td>FLOps</td><td>Floating Point Operations</td><td>SSL</td><td>Self Supervised Learning</td></tr><tr><td>fMRI</td><td>Functional Medical Resonance Imaging</td><td>SMBO</td><td>Sequential Model Based Optimization</td></tr><tr><td>GA</td><td>Genetic Algorithm</td><td>SS</td><td>Synchronous Search Strategy</td></tr><tr><td>GAN</td><td>Generative Adversarial Networks</td><td>SSC-NAS</td><td>Self-Supervised Contrastive NAS</td></tr><tr><td>GB</td><td>Gradient Based</td><td>SRGAN</td><td>Super Resolution Generative Adversarial Network</td></tr><tr><td>GNN</td><td>Graph Neural Network</td><td>TB</td><td>Transformer Based relation-wise searching approach</td></tr><tr><td>GP</td><td>Gaussian Process</td><td>TSPE</td><td>Tree Structured Parzen Estimator</td></tr><tr><td>GrS</td><td>Greedy Search</td><td>TPOT</td><td>Tree-based Pipeline Optimization Tool</td></tr><tr><td>GS</td><td>Grid Search</td><td>WSI</td><td>Whole Slide Images</td></tr><tr><td>GE</td><td>Grammatical Evolution</td><td>ZCP</td><td>Zero Cost Proxies</td></tr><tr><td>HPO</td><td>Hyper-parameter Optimization</td><td>ZC</td><td>Zero Cost</td></tr></tbody></table>

List of abbreviations used in paper

Then, the irrelevant articles are excluded based on the criteria mentioned in Table 2. For analyzing the year-wise trend of adoption of AutoML in medical image analysis, the distribution of published articles related to AutoML for medical image applications is shown in Figure 4(a). It can be observed from the graph that the literature for AutoML in medical image applications has grown throughout 2018–2023, and a large number of AutoML and NAS-based approaches have been proposed to solve different medical image analysis problems, which have an increased trend.

Furthermore, the distribution of published articles concerning different medical imaging problems is shown in Figure 4(b). It is shown that most of the AutoML approaches have been proposed to solve segmentation and classification tasks. They are considered one of the crucial medical imaging tasks as they assist in accurate and efficient diagnosis, treatment planning, and monitoring of diseases, leading to better patient care. Furthermore, these tasks serve as pretasks for radiomics analysis, uncertainty quantization, automatic reports generation, etc. The distribution of search strategies adopted by different studies is shown as a pie chart in Figure 4(c).

### 3. Overview of AutoML

AutoML is defined as a combination of automation and ML. This automation can be achieved by using optimization algorithms such as evolutionary, gradient-based, reinforcement learning, or other approaches. AutoML allows developers and data scientists to build and test models rapidly. Furthermore, it can also help reduce the time and cost required for manual machine-learning processes. However, it is essential to note that AutoML is not a replacement for human expertise. It is still necessary for experienced data scientists to oversee the process and make decisions based on their knowledge and intuition.

AutoML pipeline consists of multiple steps: data preparation, feature engineering, model generation, and model performance estimation. The data is cleaned during the data preparation step, and irrelevant features are removed. Then, in the feature engineering stage, the relevant features are selected. If necessary, features are also constructed. Subsequently, in the model generation stage, relevant models are selected using AutoML approaches, followed by tuning the model parameters. In the case of a deep learning model, a given architecture is automatically designed. Different performance estimation strategies are also used to reduce the
<figure><img src="image_7.png" /><figcaption>(a) Year-wise statistics of research papers published related to AutoML in medical image applications according to the inclusion criteria defined in this article</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(b) Bar plot indicating the number of articles categorized by medical image analysis tasks related to AutoML</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(c) Pie chart indicating the percentage of studies that have adopted different search strategies</figcaption></figure>

<caption>**Figure 4:** Summary of statistics and strategies used in AutoML research for medical imaging: (a) Year-wise publication statistics, (b) Statistics of task-wise categorization of articles, (c) Search strategies utilized.</caption>

searching time. The whole pipeline of AutoML is shown in Figure 5

## 3.1. Types of AutoML

As the goal of AutoML is to build state-of-the-art algorithms for an automated machine learning pipeline, it involves multiple tasks. Specifically, in the context of medical imaging tasks, the primary involved tasks are NAS, HPO, and ADA.

Deep learning approaches are mainly used to solve different medical image analysis tasks. AutoML tasks associated with designing and tuning deep learning architectures are HPO, ADA, and NAS, the most active sub-area of AutoML. Numerous studies have incorporated them to optimize different medical imaging tasks, e.g., segmentation, classification, registration, etc [99]. HPO-based AutoML approaches automatically find the set of parameters for a given task. NAS approaches automated network engineering by finding the best-performing neural network for a given task. NAS can also be formulated as a HPO problem as

it finds the best set of parameters for a given architecture. A detailed overview of NAS, HPO, and ADA for medical imaging tasks with their advantages and disadvantages is given in the following sections.

### 3.1.1. Neural Architecture Search

NAS refers to the automatic generation of neural networks. It learns a network topology that can achieve the best performance on a specific task. An automated designing of deep learning architectures has been extensively studied by different studies during the last two decades [81] [198]. Designing these architectures requires substantial computational resources because it combines various layers and parameters to obtain the best-performing architecture. Recently, the availability of cheap computational resources, especially graphical processing units (GPUs) has accelerated the search process within NAS and AutoML approaches.

Indeed, NAS has been used to design encoder-decoder-based U-shaped and classification architectures for medical image analysis tasks [175][9]. However, applying NAS to
<figure><img src="image_10.png" /><figcaption>Figure 5: Complete pipeline of AutoML systems. The original illustration, from Ref. [73], has been modified slightly to reflect the context of the current paper.</figcaption></figure>

medical images is costly due to the large number of samples in the data and the numerous function evaluations needed to assess the performance of deep learning architectures. These approaches require important hardware resources (multiple GPUs to be utilized for 2 to 4 weeks). In order to overcome this issue, the proposal of multiple performance estimation strategies has significantly reduced the search time. The following sections provide a detailed discussion of these approaches. Recently proposed approaches have utilized these performance estimation strategies and reduced the search time from approximately 50 days to 1 day with a minimal reduction in performance [177].

This NAS mainly consists of three components: $S_{space}$ (search space), $S_{strategy}$ (search strategy), and $P$ (performance estimation strategy), as shown in Figure 6. Let $\mathcal{A}$ be a single network in the search space $S_{space}$. Let $\mathcal{D}_{train}$ and $\mathcal{D}_{val}$ be two subsets of the datasets belonging to the training and validation sets, respectively. The performance of an architecture $\mathcal{P}_{\mathcal{A}}$, trained on $\mathcal{D}_{train}$ and validated on $\mathcal{D}_{val}$, is measured by a function $\mathcal{F}$. NAS is mathematically formulated as follows:

$$ \operatorname{argmax}_{\mathit{Perf}_{\mathcal{A}}} = \mathcal{F}(D^{\mathit{val}}(D^{\mathit{train}}(A))) \quad (1) $$

### Search Space

The NAS search space is the set of operations that can be used to build neural architectures. The size of the

<figure><img src="image_11.png" /><figcaption>Figure 6: General Process of Neural Architecture Search</figcaption></figure>

search space varies for different scenarios. There are different kinds of search spaces, such as cell-based, entire-structures, hierarchical, and morphological-based. [147]

### Architecture Optimization Method

This method uses optimization algorithms and defines how to find the optimal architecture from the search space and guides the search to efficiently find the high-performing architecture [147].
## Performance Estimation Strategy

Once the model is generated, the next important step is to evaluate the architecture's performance. One of the simplest approaches is to train the architecture to converge on the training set and evaluate the performance on the test set. However, this approach takes time. To overcome this problem, multiple performance estimation approaches, such as the weight-sharing approach, one-shot model, learning curve, and zero-shot proxy methods, have been proposed. [147]

### 3.1.2. Hyperparameter Optimization

Hyper-parameter optimization is the process of choosing the best set of parameters to solve a given problem within a significant amount of time. It can be formalized as an optimization problem:

Minimize/Maximize: $f(\mathbf{h})$

Subject to: $g_i(\mathbf{h}) \le 0$ for $i = 1, 2, ..., m$

Whereas:

* $f(\mathbf{h})$ represents the model's performance metric to be optimized.

* **h** contains the hyperparameter vector we want to optimize which contains all the hyperparameters.

* $g_i(\mathbf{h})$ are optional constraints on the hyperparameters.

* The goal is to find the best hyperparameter values $\mathbf{h}^*$ that optimize $f(\mathbf{h})$ while satisfying the constraints.

Numerous optimization approaches have been proposed to solve HPO problems. Some of the famous HPO approaches are based on grid search, random search, Bayesian Optimization (BO), evolutionary-based, and gradient-based approaches [20].

### 3.1.3. Automatic Data Augmentation

Data Augmentation have proven to be very effective for reducing overfitting. It increases the size of data by applying morphological and other rotation/scaling operations on existing data. Nevertheless, selecting a relevant data augmentation technique for a specific problem is a challenging task. ADA tries to find a specific augmentation policy that is suitable for a given problem, where the aim is to increase the performance [35]. ADA improves significantly the performance of medical image analysis tasks by enhancing model generalization towards unseen data and resolving limited data problems. Since it is very difficult to search for an optimal set of augmentations and a single strategy would not perform well on multiple datasets, it has been shown that ADA can be extremely useful [184]. Compared to manual tuning and selection of augmentation parameters, ADA automatically picks the best set of augmentations for a given dataset. A general overview of ADA is shown in Figure 7. A general ADA framework consists mainly of two main phases (i) Augmentation Phase and (ii) Classification Phase. ADA algorithms select the best set of augmentations for given data. Then, it trains a model on the augmented ones to get the prediction as a feedback. This step is repeated until the stopping criteria is reached.

## 3.2. Components of AutoML

Generally, every AutoML system consists mainly of three main components: (i) Search Space (ii) Search Strategy and (iii) Performance Estimation Strategy. These components work together to automate the whole AutoML process.

### 3.2.1. Search Space

The search space defines the set of possible configurations which AutoML system can explore to find the best model or set of parameters. The process includes various hyper-parameters such as architectural hyper-parameters of a neural network (e.g., type of operations, number of layers, connections between the operations, etc.) and data augmentations defined. There are various types of hyper-parameters, including categorical and numerical hyper-parameters. On the one hand, categorical hyper-parameters can take a discrete set of values, such as the type of optimizer used for the neural network training (Adam, SGD, AdaGrad etc.).

On the other hand, numerical hyper-parameters are defined in the numerical domain, which represents values as a set of positive integers or real values. These hyper-parameters typically have a range predefined by the developer. Following the same context, the task of AutoML system is to search for a best set of parameters from the search space at hand. Similarly, in the case of ADA, the augmentation types are represented with a categorical format whereas augmentation parameters are represented with a numerical format.

The search space representation have a direct impact on the performance of NAS [10]. The NAS search space represents the set of operations (convolutions, pooling, etc.) for the formation of deep learning architectures. It defines a set of operations (pooling layers, skip-connection, convolution operations, dense layers) and how these layers are interconnected. In recent years, different types of search spaces have been proposed. Most of the work on the NAS search space focuses on the automatic design of architectures for image recognition tasks (segmentation, classification and reconstruction). Two main categories of NAS architecture search spaces are proposed for Image classification and segmentation tasks named micro and macro search space, as shown in Figure 8. The macro architecture determines the overall backbone of the network, and the micro architecture determines the structure of each network unit. Often, the micro architecture is referred to as cells, which consists of different deep learning operations called nodes, and the number of cells positioned in a chain structure is called macro search space. The number of cells can vary according to different tasks. NAS algorithms can either search for micro or macro search space but not necessarily both. Nowadays, most NAS approaches fix the structure of macro search space, also called cell-based search space, and search for the micro search space (cells).

It can be noticed that several studies focusing on NAS for medical image applications use cell-based search space thanks to its ability to use a wide range of operations and flexibility in modification [158]. NAS-Net [221] is one of
<figure><img src="image_12.png" /><figcaption>**Figure 7:** General process of Automatic Data Augmentation (ADA) framework used in different studies. Augment policy selects the best augmentation techniques for data and the performance of classification network trained on combined original and augmented data is used as a feedback. Then, another augmentation policy is sampled. This step is repeated until a stopping criteria is reached. One of the challenging aspects of ADA is the performance evaluation of the augmented data.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 8:** An example of Micro vs Macro search space (a) Macros architecture where each cell receives input from two precursors and (b) Micro architecture where each node have multiple connections enabling inter-node connections in the block</figcaption></figure>

the first propositions to use cell-based neural architectures. Another famous search space is Differentiable ARchiTecture Search (DARTS), which extends the NAS-Net search space. Two different types of cells are involved in this representation by [221] (normal and reduction cells). The normal cell keeps the same spatial resolution, and the reduction cell reduces the resolution. In NAS, the searching algorithm designs neural networks by stacking multiple cells as shown in Figure 9. This cell-based search space is further modified

to build FCN-based encoder-decoder architectures. In order to use this search space for U-net-shaped encoder-decoder architectures, [153] proposed AutoDispNet, which utilizes a search space consisting of three different types of cells: normal, reduction, and upsampling.

<figure><img src="image_14.png" /><figcaption>**Figure 9:** NASNet Search Space consisting of normal and reduction cells</figcaption></figure>

The encoder consists of multiple normal and reduction cells, whereas the decoder consists of multiple up-sampling cells. More details regarding these cell-based search spaces are theoretically and experimentally studied in [158].

### 3.2.2. Search Strategy

Given a search space, there are multiple methods to search for the best set of hyper-parameters or network architectures for a given task. The search strategy explores the search space to find the best performing configuration. Mainly these search strategies are categorized into three
<figure><img src="image_15.png" /><figcaption>**Figure 10:** The overall framework of Neural Architecture Search with Reinforcement Learning. The controller samples an architecture and based on the reward accuracy R samples new architecture string and give high probability to architectures having better architecture.</figcaption></figure>

types: (i) reinforcement learning algorithms (ii) gradient-based algorithms and (iii) evolutionary algorithms. However, some studies also explored other searching techniques, such as BO, random search, sequential Model Based Optimization (SMBO) [178] etc. We briefly describe some of the famous search strategies used for NAS, HPO and ADA.

**Reinforcement Learning based Approaches** Reinforcement Learning (RL) methods have been widely used to search for data augmentation policies for given dataset especially in medical imaging tasks. They have been widely used due to its flexibility in exploring diverse augmentation policies, adaptability based on the feedback and ability to balance exploration and exploitation. Recently they have been used in NAS as a search strategy. One of the early NAS approach is proposed by Zoph et al. [221] which use reinforcement learning as a search strategy to automatically design an architecture. They formulate the NAS problem as a reinforcement-learning problem. The structure and connectivity of the neural network is represented as a string generated by a RNN-based controller. The RNN samples a simple Convolutional Neural Network (CNN) that predicts the number of filters, height, width of each filter and stride. It predicts these parameters for one layer while repeating the process. While, a softmax classifier performs a prediction and forwards it to the next step as an input as shown in Figure 10.

**Gradient-based Approaches** Gradient-based approaches have shown to be very effective in the case of NAS and ADA, in terms of performance and search time. Searching for an optimal data augmentation for segmentation and classification can be formulated as a gradient-based optimization. This latter has been adopted in multiple studies by searching both network weights and augmentation parameters [208].

Liu et al. [100] proposed DARTS that optimizes the continuous relaxation of discrete decision variables of a bi-level optimization problem as shown in Figure 11. The continuous variables represent the probabilities of selecting each operation. They use softmax to sample categorical

choices of particular operations among possible operations. The goal of using softmax is to jointly learn the architecture A and the weights within all mixed operations, as shown in Figure 11. Furthermore, a weight-sharing mechanism is used across different operations to reduce the search and training time [206].

**Evolutionary Algorithms based Approaches** Evolutionary algorithms have been successfully adopted as a search strategy to optimize different AutoML tasks including NAS, HPO and ADA for numerous medical imaging tasks. Numerous studies have demonstrated that evolutionary algorithms play a vital role in NAS [106]. They use natural phenomena-based search operators to evolve individuals in order to find promising solutions. Evolutionary algorithms are designed to solve both single and multi-objective optimization problems. The visual representation of the general overview of evolutionary NAS approach is shown in Figure 12. First, the population is generated randomly by sampling architectures from the search space while the population is represented by a predefined encoding format, e.g., graph-based representation, matrix representation, discrete representation, etc. Each individual is decoded into a neural network model. Then, it is trained to get a fitness value. Afterwards, new individuals are generated by reproduction (crossover and mutation) operators using the best-performing individuals in the population over multiple generations. The reproduction operators aim to achieve a reasonable balance between exploration and exploitation to find the best-performing individuals. Although evolutionary NAS search process is effective, it is time-consuming and not efficient in terms of resources. To overcome this problem, one-shot, surrogate and zero-cost proxy methods have been proposed, which are explained in "Performance Estimation Strategy" section. For more information related to evolutionary NAS, the survey papers of Zhou et al. [216] and Liu et al. [106] are suggested.
<figure><img src="image_16.png" /><figcaption>**Figure 11:** An example of continuous relaxation in DARTS. (a) There are possible connections (operations (e.g., convolution, pooling, identity)) between nodes (layers) 0 to 3 which need to be searched. (b) Between each node, there are multiple edges with distinct weights represented with different colors. (c) The minimum path based on the weight of an edge is highlighted. The softmax is applied to these parameters to obtain probability distribution over the operations on each edge. (d) The operations with higher probability (indicated by thicker arrows) are highlighted to indicate the final architecture.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>**Figure 12:** Overview of Evolutionary Neural Architecture Search Approach. It consists of four main stages (i) Population Initialization (i) Evolution Phase (iii) Reproduction and (iv) Environment selection. In the population initialization stage, the population is randomly generated and evaluated. In the reproduction stage the offspring's are generated by applying crossover and mutation operations. Then the fitness of the offspring's is computed and based on their fitness values, they are added in the population or discarded. This step is repeated until the stopping criteria is met. The benefit of evolutionary NAS approach is it keeps the diversity among individuals by generating offspring's and keep balance between exploration and exploitation. The disadvantage is that the fitness evaluation is very time consuming process.</figcaption></figure>

**Bayesian Optimization approaches** Bayesian Optimization (BO) is a global optimization strategy used for black-box optimization problems [53]. It uses a surrogate model and approximation function to map a number of hyper-parameters to model the performance. Following the state-of-the-art propositions, it can be noticed that BO has been used for HPO and NAS based approaches [176].

It is effective for hyperparameter optimization (HPO) due to its efficient use of expensive function evaluations and its ability to balance exploration and exploitation. Additionally, its acquisition function decides where to sample

next, designed to balance the exploration of the search space (finding new and uncertain parameter configurations) and exploitation (focusing on areas where the model predicts high performance). It leverages prior knowledge and updates with sequential, informed choices to quickly converge towards optimal settings.

In this context, Sequential Model-Based Optimization (SMBO) is a well-known technique that introduces the Tree Parzen Estimator (TPE), which models the objective function as a probability distribution. SMBO is also used in various Neural Architecture Search (NAS) approaches. It
aims to find the architecture A that maximizes $f(A)$, where $f(A)$ represents the performance of the architecture A. SMBO defines a probabilistic inference model of the search space and guides it toward efficient architectures.

### 3.2.3. Performance Estimation Strategies

One of the key issue with NAS approaches is their extensive time requirement for evaluation of the performance of neural networks. To overcome this problem, multiple performance estimation strategies have been proposed in the literature that estimate the performance of neural networks. We give a short overview of these approaches investigated in the literature.

1. One-Shot approaches: In one-shot NAS, a huge super-network is trained consisting of all possible operations in the search space. During the search process, multiple sub-networks are sampled from the super-network. The weights of sub-networks and super-networks are shared, which saves the time required to train the sub-networks [181].

2. Surrogate-based approaches: Surrogate models have been used in both HPO and NAS to reduce the searching time [111] [21]. This hybridization involves training a surrogate model and used to predict the performance of a candidate architecture or hyperparameters settings to save the evaluation time. The surrogate model is first trained on the individual representation (architectures represented in a predefined encoding format) and their fitness values. This approach is quite efficient but its only limitation is that the surrogate model performance is based on the amount of training data.

3. Early Stopping: Early stopping is one of the simplest performance estimation strategies in which, each candidate architecture is trained until its validation accuracy stops improving. This strategy also called as low-fidelity evaluation is straightforward. This approach is most commonly used in NAS, HPO and ADA. However, it often leads to an underestimation of the performance since certain architectures require more training iterations to reach convergence [32][1].

4. Zero-Cost proxies methods: Zero-cost proxies are a reduced form of training that use a single mini-batch of data to score a deep learning architecture. These matrices are inspired by recent pruning-at-initialization state-of-the-art propositions and modified to score an entire neural network to work with NAS [2].

For a detailed discussion regarding performance estimation strategies, the article by white et al. [177] is suggested.

## 4. AutoML in Medical Image Analysis

As discussed previously, AutoML has been widely used to solve different medical image analysis tasks, including medical image classification, segmentation, denoising, etc. In this section, we have briefly described these approaches with respect to different optimization strategies.

### 4.1. AutoML based Medical Image Classification Approaches

Studies have proposed numerous approaches for medical image classification using various AutoML tasks. We have classified these approaches based on their search strategies. Several evolutionary approaches have been proposed for automatically designing deep learning architectures for medical image classification. These approaches consist of single or multiple objectives, which uses error rate or accuracy as a single objective and network size, latency, and the number of FLOPs for multiple objectives.

#### 4.1.1. Single Objective Evolutionary approaches

Studies have proposed multiple single-objective medical image classification approaches utilizing meta-heuristics and grammar-based evolutionary search strategies. Meta-heuristic algorithms are high-level heuristics that are problem-independent and proposed to solve various optimization problems. Heuristic algorithms are specially designed functions to solve specific problems. Famous meta-heuristics include Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Most of these meta-heuristics are nature-inspired and mimic natural behavior [195].

There are two main types of metaheuristics : (i) single solution-based and (ii) population-based. A single solution-based meta-heuristic, also called local search, employs a single solution and tries to improve this solution in its neighborhood. In contrast, population-based meta-heuristics use a set of potential solutions, called population, to explore the search space. Both types of meta-heuristics have been employed to search for an architecture for numerous medical imaging tasks [164]. Following the same context, Grammatical Evolution (GE) is a family of evolutionary algorithms that uses grammar-based representation to evolve solutions to a given problem. In GE, each solution is represented as strings of symbols, and context-free grammar is defined, which describes syntax rules [128]. On the other hand, Genetic Programming (GP) employs tree-based representation to evolve the solutions [3].

Several local search approaches have been also proposed. Local search-based NAS approaches modify a single individual Neural Network (NN) representation either width- or depth-wise in each iteration by increasing the number of layers and trying different combinations of layers over the iterations. This approach is called network morphism, as the NN is modified in every iteration. In [23], a mixed-block-based neural architecture search approach is proposed using a local search algorithm to classify X-ray images in [141]. Apart from this, local search-based AutoML frameworks are also used, such as Ma et al. used AutoKeras to automatically design CNN architecture for distinguishing benign and malignant lesions on breast cone-beam (CT) scans [113]. Furthermore, the Hill-climbing algorithm is also used in [92] to solve the medical melanoma detection approach. Some studies have used simulated annealing for optimizing CNN architecture parameters for classification problems [98].
Indeed, local search approaches are easy to implement and do not rely on any prior knowledge of the problem at hand. However, one of their drawbacks is their poor exploration of the search space. The performed small perturbations around existing architectures may focus only on a small search space region increasing the possibility of getting stuck into a local optimum.

Studies have also used different metaheuristics to search for an architecture for medical image classification. Most of these studies use the GA thanks to its straight-forward nature. GA is used to search for an algorithm to assess the quality of human sperm, detecting abnormalities in breast images and medical image classifications [117] [58] [6] [18]. In [117], Miahi et al. proposed a method for assessing human sperm that uses NAS and a GA named GeNASWF. Their proposed approach first eliminates the irrelevant layers from the architecture using pruning. Then, they predict the validation accuracy on different mini-batches and multiply them to get an average cross-correlation score. This score is then weighted by a factor called alpha, which balances the importance of the best individual model with the average performance of the population.

Furthermore, in [6], a GA-based approach based on a variable-length encoding scheme is proposed. Their search space consists of convolution layers, multiple pooling layers, and attention layers. They performed experiments on the MedMNIST dataset. Their fitness function is mono-objective, combining various objectives, i.e., FLOPs, accuracy, and F-1 score, using a weighted-sum approach. A three-point crossover operator is used for reproduction. The proposed fitness function enables the approach to easily converge during the search process but lacks of pareto optimality. Bevila et al. also proposed to use a GA with a mono-objective function consisting of several iterations and accuracy to optimize the architecture of ANN for breast cancer classification [18]. Their fitness function consists of an average accuracy value given all the iterations. Besides this, Ghosh et al. proposed a two-phase evolution CNN architecture for medical image classification [58]. Their proposed approach first searches for architecture topology, and in the second stage, the hyperparameters of designed CNN architectures are fine-tuned with GA.

Besides GA, other meta-heuristics have also been employed to analyze medical images. Oyelade et al. proposed an approach for detecting abnormalities in histopathology images from NAS with block-based search space [125]. They designed a block-based stochastic categorical-to-binary algorithm for generating CNN solutions in the search space. The block-based encoding scheme consists of block-based representations for hyper-parameters, convolution, pooling layers, etc. Moreover, they used the EBOLA search algorithm to find potential architectures. Furthermore, a Differential Evolution (DE) approach is proposed to search

for CNN architectures. They formulated the problem to represent CNN architectures using fixed length encoding representation and performed experiments on three datasets containing Magnetic Resonance Imaging (MRI) scans, histopathology images, and ultrasound images [15]. Moreover, Junaid et. al proposed and evolutionary NAS approach based on DE algorithm to search for robust architectures resilient against different adversarial attacks on medical images [9]. They have performed experiments on multiple MedMNIST datasets and the comparison shows that the effectiveness of their proposed approach

Although these approaches are quite effective in finding deep learning architectures for given task, their main disadvantage is the searching time. To overcome this problem different performance estimation strategies have been used to reduce the function evaluation time. Mostly surrogate models have been used such as, in [7] Ali et al. proposed a surrogate-assisted genetic algorithm to reduce searching time for medical image classification. Their proposed approach uses a continuous encoding scheme for the representation of deep neural networks. For surrogate-assisted prediction, XG-boost and random forest models predict an individual's fitness during searching. Also, in [44], a surrogate-based GA approach is used to evaluate the architectures based on the average performance of different random seeds and cross-validation to overcome the noise issue. For searching, they used a Surrogate-assisted Genetic Pool Optimal Mixing Evolutionary Algorithm (SGOMEA), which is a surrogate model for cheap fitness evaluation with GOMEA [46].

Some of these studies focus only on specific diseases, e.g., brain tumor classification, breast cancer detection, etc., and used only a single dataset [125] [18] [117], which does not show the ability of generalization of their approach towards multiple tasks. However, some studies have performed experiments on multiple datasets consisting of different modalities and tasks to demonstrate the effectiveness of their approach [58] [6] [7], while showing that performance estimation strategies are effective in terms of performance.

### 4.1.2. Multi Objective Evolutionary approaches

Studies have proposed multiple multi-objective NAS approaches to solve medical image classification tasks. In [187], a multi-objective approach for multi-organ classification is proposed. They have performed experiments on multi-organ datasets and achieved promising results. Similarly, an "architecture entropy" sampling strategy-based approach is proposed in [36] to maintain diversity among individuals. By incorporating mechanisms to enhance diversity, the proposed approach searches for an architecture in fewer epochs. However, the approach is computationally expensive due to the need of evaluating numerous candidate architectures and maintain population diversity.

The benefit of using multi-objective approaches is to overcome conflicting objectives by finding architectures with fewer parameters and a reasonable accuracy. Although
these approaches are effective, they remain computationally
expensive due to handling multiple objectives.

Several performance estimation strategies are also em-
bedded with evolutionary approaches to designing efficient
NAS algorithms to overcome this problem. In [209], a multi-
objective NAS approach is proposed based on Zero Cost
(ZC) proxies for medical image classification. They used a
multi-objective NSGA-II algorithm to search architectures
with good classification accuracy and fewer parameters.
They used ZC proxies for fitness evaluation, which estimate
the architecture's performance. Their proposed approach is
quite efficient regarding time complexity and searches for an
architecture within one hour.

A recent work by Yan et al. proposed a new approach for
medical image classification called multi-objective, multi-
scale NAS [171]. Their approach uses a multi-scale reduction-
based search space to extract features from multiple levels
and fuse them to learn multi-resolution features. For fitness
evaluation, they used multiple ZC proxies, and a multi-
objective evolutionary algorithm is used to search for ar-
chitectures that perform well while consuming less memory.
The researchers tested their approach on the 2D MedMNIST
dataset and achieved state-of-the-art performance. Their ex-
periments demonstrated the effectiveness of the approach in
improving the classification accuracy of medical images. It
can be noted that adopting ZC proxies reduces significantly
the search time. Besides, the multi-objective algorithm could
efficiently explore the search space.

In another context, He et al. [72] proposed a weight-
sharing NAS approach for medical image classification and
a multi-objective approach for analyzing COVID-19 scans
in [59]. They proposed using an objective function, namely
potential with accuracy and model size, that predicts an
individual quality by incorporating prior performance. Al-
though the weight-sharing approach reduces the search time
required to evaluate architectures, its biased behaviour to-
wards exploitation may not lead to a global optimal solution.

Grammar-based approaches can also effectively search
for an architectural topology for medical image classifica-
tion. Da et al. formulated the NAS problem as a grammar
for representing the encoding of CNN architecture using
context-free grammar alongside different objective func-
tions for fitness evaluation [38].

The joint search of HPO and NAS has been studied in
recent years. Most NAS approaches assume fixed hyperpa-
rameters, but some studies have proposed a joint search of
NAS+HPO and treated it as a multi-objective optimization
approach [60]. Only a few studies jointly optimize HPO
and NAS, as training the model on medical images takes
much time. In [96], the authors proposed an evolutionary
approach for jointly optimizing hyperparameters and neu-
ral network techniques for classification. They named their
approach LEAF, which utilizes state-of-the-art evolutionary
algorithms and distributed frameworks. Furthermore, they
minimize the evaluation time by searching only on limited
samples to search for the best-performing architecture with

the best setting of hyper-parameters and then training on all
the datasets.

Multiple AutoML approaches that combine FL with NAS to increase medical data security have been proposed in the literature. These approaches search for an architecture and then aggregate the model weights from predictions of data sub-sets from the NAS-searched model with FL for better data security. FL is a machine learning technique that deals with training algorithms across multiple decentralized devices or servers holding local data. They only shared the model weights with the central server, aggregating weights on the central server. FL is a very effective approach for securely sharing local Internet of Things (IoT) and medical data from multiple sources into one centralized server. FL is found to be very effective in the medical domain, as sharing important patient information is not possible in most cases due to privacy concerns, and FL provides an important pipeline to share the model weights instead of sharing the data [150]. Similarly, a Multi-Objective NAS approach utilizing the Multi Objective Evolutionary Algorithm with Decomposition (MOEA/D) algorithm is also proposed with FL in medical imaging. They proposed a multi-objective NAS utilizing the MOEA/D algorithm for designing the CNN architecture and the fuzzy layer within the CNN architecture to improve the interoperability [105]. They have performed experiments on different variants of NSGA, such as NSGA-II, NSGA-III, MOEA/D, and MOEA/D variants, which use the normalized Chebyshev approach. They also improve the selection strategy and introduce utility to quantify the probability in the neighborhood to improve the exploration ability. Incorporating FL with NAS for medical images overcomes the shift problem caused by data from multiple sources. However, the main issue of this approach is the highly important search time required within its process.

FL assists the healthcare institutions to share there data
while keeping their privacy. One of the main problem with
FL is that each healthcare institution data have their own
distribution and when models weights from these institutions
are aggregated, their may be performance degradation. FL
NAS assists in finding efficient architectures across multiple
local institutions and then these searched architectures are
then aggregated at global point to make one architecture
which learns data distributions of all institutions and makes a
generalized model. The benefits of using multi-objective ap-
proaches for FL NAS is two fold. It helps in searching for an
architecture for given task having less number of parameters.
However, the main problem with these approaches is that
as the NAS algorithm have to learn weights from multiple
sources it increases the search time, which can be improved
by incorporating performance estimation strategies to make
them more efficient.

4.1.3. Gradient-based approaches

Studies have proposed different gradient-based approaches to design architecture for medical image classification. These approaches extend the DARTS NAS framework for medical image classification tasks.
A probable DARTS variant is proposed for histopathology image classification [165]. This study uses a probing metric to overcome the hyper-parameter tuning problem and generalization issue. The proposed adaptive strategy leads to performance improvement. The main drawback of this approach is that using an adaptive optimization strategy may introduce additional complexity to the model development process and is computationally expensive. Recent studies have also proposed a NAS approach for searching architecture for multi-label medical image classification problems. Yang et al. proposed a multi-objective NAS algorithm to classify chest radiography images [196]. They used a gradient-based One-Shot NAS approach to search for an architecture. The advantage of gradient-based approaches is their relatively low search time thanks to the continuous relaxation and weight sharing. Nevertheless, gradient-based approaches tend to get stuck into local optimal solutions because of their limited capability of exploring the search space.

Moreover, peng et al. proposed a multi-modality NAS approach to predict the development of distant metastases from PET-CT scans [131]. They proposed a multi-modality fusion approach by combining multiple modalities to enhance the feature extraction. The proposed approach achieves promising results through multiple modality information fusion and automatic architecture search. In another context, Data augmentation and architecture search are both formulated as a gradient-based problem and jointly searched using a single-path differentiable search algorithm [70]. Blockchain is also combined with NAS for better data security. Yang et al. incorporated blockchain technology with NAS for pneumonia image classification for better data security [197].

### 4.1.4. Discussion

The comparison of AutoML-based medical image classification approaches is given in Table 5. We have categorized these approaches based on their search strategies and reported their average search time (in hours). These approaches use NAS for automatic architecture search, ADA for learning augmentation policy, HPO for optimizing architecture hyperparameters and automatic pruning of the searched architectures. The adoption of search strategies by these AutoML approaches greatly impacts the performance and quality of searched individuals. Different optimization approaches have been used for the search strategy, i.e., gradient-based approaches, evolutionary reinforcement, and fusions of different approaches. The approaches requiring less search time among the optimization strategies are highlighted in green color.

The performance comparison of AutoML approaches for medical image classification is quite challenging because these studies have used different datasets, which makes it very difficult to compare the performance of these approaches. To overcome this problem, we have separately

compared studies that have used the MedMNIST benchmark. MedMNIST is a famous benchmark designed to evaluate the performance of different AutoML approaches. In Table 4, the comparison of reported accuracy, AUC scores, and searching time (in hours) of these approaches is given. Due to the large number of samples in MedMNIST's multiple datasets, the fitness evaluation is time-consuming because it requires training and evaluating each individual. Therefore, performance prediction strategies are effective and require relatively a lower search time compared to other approaches. The best reported results are highlighted in green color.

Among these performance estimation strategies, ZC proxies and one-shot NAS approaches are quite effective in terms of performance and searching time compared to other approaches. As in one-shot NAS approaches, a weight-sharing mechanism is used while sampling sub-nets from super networks. Several gradient-based, single, and multi-objective evolutionary supernet approaches have been proposed for medical image classification, which are quite effective in performance and searching cost. Multi-objective evolutionary algorithms are very effective in searching architecture, having good performance but less latency and number of parameters, as they are very effective in cases of conflicting multiple objectives. Apart from this, ZC proxies have less searching time because they evaluate each architecture on a small batch of data. However, there is a sacrifice of performance in dealing with less searching cost, which makes it a trade-off between accuracy and searching time. Combining ZC proxies with multi-objective evolutionary algorithms performs well with less search time.

Moreover, it is also worth noting that data augmentation is also very effective among NAS-based medical image classification problems, as some studies search for both architectural topology and data augmentation strategies together with single-path SuperNet using gradient-based (GB) and EA. Local search approaches are ineffective in medical image classification as they perform more exploitation of individuals instead of exploration, which is quite necessary in the case of NAS. Secondly, these approaches are quite effective when used with NAS benchmarks because these benchmarks contain model representations and their fitness values and don't require time to evaluate their performance. However, in the case of medical imaging tasks, each model's evaluation is necessary to obtain its fitness value. It would be a very interesting future direction to combine multiple performance estimation strategies, such as zero-cost proxies and surrogate approaches, with one-shot NAS approaches. Moreover, applying these approaches to large-scale medical datasets regarding hardware efficiency is also an exciting direction, as designing a lightweight, optimal architecture for a large-scale medical dataset is still a challenging problem.

Another notable aspect is that most of the studies focus on 2D medical image classification compared to 3D medical image classification. Medical data consists of 3D volumes consisting of multiple slices in MRI, CT, and other modalities. It would be a significant contribution to propose
<table><caption>**Table 4**</caption><thead><tr><th>Study</th><th></th><th>[70]</th><th>[6]</th><th>[187]</th><th>[38]</th><th>[97]</th><th>[208]</th><th>[209]</th><th>[7]</th><th>[171]</th><th>[8]</th></tr></thead><tbody><tr><td>Type of AutoML task</td><td>ADA+ NAS</td><td>NAS</td><td>NAS</td><td>NAS</td><td>NAS</td><td>NAS</td><td>NAS</td><td>NAS</td><td>NAS</td><td>NAS</td><td>NAS</td></tr><tr><td>Year</td><td>2022</td><td>2023</td><td>2022</td><td>2021</td><td>2023</td><td>2023</td><td>2023</td><td>2023</td><td>2023</td><td>2024</td><td>2024</td></tr><tr><td>Technique used</td><td>GB</td><td>SOEA</td><td>MOEA</td><td>EA</td><td>EA</td><td>EA</td><td>MOEA+ZC</td><td>SOEA</td><td>MOEA+ZC</td><td>SOEA+ZC</td><td>SOEA+ZC</td></tr><tr><td>Search time (In hours)</td><td>1.5</td><td>18</td><td>N/A</td><td>N/A</td><td>0.42</td><td>2.91</td><td>0.006</td><td>4</td><td>0.015</td><td>1</td><td>1</td></tr><tr><td rowspan="2">PathMNIST</td><td>Accuracy</td><td>0.93</td><td></td><td>0.9023</td><td>0.919</td><td>0.931</td><td>0.915</td><td>0.96</td><td>0.91</td><td>0.920</td><td></td></tr><tr><td>AUC</td><td>0.98</td><td></td><td></td><td></td><td>0.989</td><td>0.989</td><td>0.998</td><td>0.990</td><td>0.991</td><td></td></tr><tr><td rowspan="2">OCTMNIST</td><td>Accuracy</td><td></td><td></td><td>0.7652</td><td></td><td>0.833</td><td>0.826</td><td></td><td>0.780</td><td>0.800</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td>0.967</td><td>0.957</td><td></td><td>0.968</td><td>0.969</td><td></td></tr><tr><td rowspan="2">OrganAMNIST</td><td>Accuracy</td><td>0.95</td><td>0.9764</td><td>0.9283</td><td>0.953</td><td>0.959</td><td>0.958</td><td>0.96</td><td>0.962</td><td>0.957</td><td></td></tr><tr><td>AUC</td><td>0.99</td><td>0.9987</td><td></td><td></td><td>0.997</td><td>0.996</td><td>0.999</td><td>0.999</td><td>0.999</td><td></td></tr><tr><td rowspan="2">Organ3D</td><td>Accuracy</td><td>0.964</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.908</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.995</td><td></td></tr><tr><td rowspan="2">Nodule3D</td><td>Accuracy</td><td>0.913</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.877</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.871</td><td></td></tr><tr><td rowspan="2">Fracture3D</td><td>Accuracy</td><td>0.575</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.690</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.728</td><td></td></tr><tr><td rowspan="2">Adrenal3D</td><td>Accuracy</td><td>0.826</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.805</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.857</td><td></td></tr><tr><td rowspan="2">Vessel3D</td><td>Accuracy</td><td>0.95</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.940</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.940</td><td></td></tr><tr><td rowspan="2">Synapse3D</td><td>Accuracy</td><td>0.801</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.846</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.820</td><td></td></tr><tr><td rowspan="2">OrganCMNIST</td><td>Accuracy</td><td>0.92</td><td>0.9495</td><td></td><td>0.925</td><td>0.926</td><td>0.929</td><td></td><td>0.936</td><td>0.935</td><td></td></tr><tr><td>AUC</td><td>0.99</td><td>0.995</td><td></td><td></td><td>0.911</td><td>0.992</td><td></td><td>0.996</td><td>0.997</td><td></td></tr><tr><td rowspan="2">OrganSMNIST</td><td>Accuracy</td><td>0.94</td><td>0.8736</td><td></td><td>0.818</td><td>0.838</td><td>0.833</td><td></td><td>0.838</td><td>0.864</td><td></td></tr><tr><td>AUC</td><td>0.98</td><td>0.9892</td><td></td><td></td><td>0.981</td><td>0.979</td><td></td><td>0.983</td><td>0.984</td><td></td></tr><tr><td rowspan="2">BreastMNIST</td><td>Accuracy</td><td>0.91</td><td></td><td></td><td></td><td>0.904</td><td></td><td></td><td>0.872</td><td>0.911</td><td></td></tr><tr><td>AUC</td><td>0.92</td><td></td><td></td><td></td><td>0.92</td><td>0.921</td><td></td><td>0.93</td><td>0.930</td><td></td></tr><tr><td rowspan="2">RetinaMNIST</td><td>Accuracy</td><td></td><td></td><td></td><td></td><td>0.550</td><td>0.58</td><td></td><td>0.55</td><td>0.561</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td>0.788</td><td>0.823</td><td>0.79</td><td>0.755</td><td>0.759</td><td></td></tr><tr><td rowspan="2">PneumoniaMNIST</td><td>Accuracy</td><td>0.9</td><td></td><td></td><td></td><td>0.942</td><td>0.941</td><td></td><td>0.912</td><td>0.944</td><td></td></tr><tr><td>AUC</td><td>0.95</td><td></td><td></td><td></td><td>0.985</td><td>0.98</td><td></td><td>0.963</td><td>0.983</td><td></td></tr><tr><td rowspan="2">DermaMNIST</td><td>Accuracy</td><td>0.77</td><td></td><td></td><td></td><td>0.768</td><td>0.779</td><td>0.75</td><td>0.774</td><td>0.769</td><td></td></tr><tr><td>AUC</td><td>0.95</td><td></td><td></td><td></td><td>0.912</td><td>0.918</td><td>0.924</td><td>0.934</td><td>0.941</td><td></td></tr><tr><td rowspan="2">ChestMNIST</td><td>Accuracy</td><td>0.93</td><td></td><td></td><td></td><td>0.948</td><td>0.948</td><td>0.949</td><td>0.945</td><td>0.949</td><td></td></tr><tr><td>AUC</td><td></td><td></td><td></td><td></td><td>0.798</td><td>0.794</td><td>0.77</td><td>0.741</td><td>0.760</td><td></td></tr><tr><td rowspan="2">TissueMNIST</td><td>Accuracy</td><td>0.67</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.74</td><td>0.760</td><td></td></tr><tr><td>AUC</td><td>0.92</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.951</td><td>0.948</td><td></td></tr><tr><td rowspan="2">BloodMNIST</td><td>Accuracy</td><td>0.990</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.976</td><td>0.967</td><td></td></tr><tr><td>AUC</td><td>0.960</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.999</td><td>0.999</td><td></td></tr></tbody></table>

Comparison of reported Accuracy and AUC scores by different studies on MedMNIST datasets. We have highlighted the best-reported results in green color.

an approach for 3D medical image classification using ZC proxies and hardware-aware metrics.

<figure><img src="image_18.png" /><figcaption>**Figure 13:** U-Net architecture consisting of encoder-decoder block</figcaption></figure>

## 4.2. AutoML based Medical image Segmentation Approaches

Image segmentation is a well-known technique in digital image processing to partition an image into multiple regions or parts based on the characteristics of image pixels to perform object detection or other relevant tasks. In the context of medical image segmentation, the aim is to segment out a specific organ or malicious/tumorous region from a medical scan. Medical image segmentation is a challenging task due to multiple factors such as class imbalance, small objects, weak boundaries, huge memory consumption of large datasets, and the processing of 3D high-resolution images. Encoder-decoder-based deep learning architectures, also known as FCN, have been used to solve medical image segmentation. A generic architecture of U-Net is shown in Figure 13.
<table><thead><tr><th>Reference</th><th>Dataset</th><th>Type of AutoML problem</th><th>Year</th><th>Type</th><th>Modality</th><th>Type of data</th><th>Organ</th><th>GPU Used</th><th>Searching time (In hours)</th></tr></thead><tbody><tr><td colspan="10" style="text-align:center; font-weight:bold;">Gradient based</td></tr><tr><td>[165]</td><td>ADP, BCSS, BASH, Osteosarcoma</td><td>NAS</td><td>2021</td><td>GB</td><td>Histopathology</td><td>2D</td><td>Multi-Organ</td><td>N/A</td><td>N/A</td></tr><tr><td>[70]</td><td>MedMNIST</td><td>ADA+NAS</td><td>2022</td><td>GB</td><td>Multiple Modalities</td><td>3D</td><td>Multi-Organ</td><td>NVIDIA V100 GPU</td><td>1.5</td></tr><tr><td>[209]</td><td>MedMNIST</td><td>NAS</td><td>2023</td><td>EA</td><td>Multiple Modalities</td><td>2D</td><td>Multi-Organ</td><td>N/A</td><td>0.007</td></tr><tr><td>[70]</td><td>MedMNIST</td><td>ADA+NAS</td><td>2022</td><td>GB</td><td>Multiple Modalities</td><td>3D</td><td>Multi-Organ</td><td>NVIDIA V100 GPU</td><td>1.5</td></tr><tr><td>[135]</td><td>US Images dataset</td><td>NAS</td><td>2022</td><td>GB</td><td>Ultrasound</td><td>2D</td><td>Liver</td><td>Nvidia Titan X GPU</td><td>1.5</td></tr><tr><td>[131]</td><td>TCIA PET-CT STSs</td><td>NAS</td><td>2020</td><td>GB</td><td>PET,CT</td><td>3D</td><td>Brain</td><td>NVIDIA GeForce GTX 2080Ti GPU</td><td>1</td></tr><tr><td>[180]</td><td>Private Dataset</td><td>NAS</td><td>2022</td><td>GB</td><td>Glioma Images</td><td>2D</td><td>Brain</td><td>Tesla P100</td><td>18</td></tr><tr><td>[197]</td><td>3 Public Pneumonia Datasets</td><td>NAS</td><td>2023</td><td>GB</td><td>X-ray</td><td>2D</td><td>Chest</td><td>N/A</td><td>N/A</td></tr><tr><td>[169]</td><td>Public OCT Dataset</td><td>NAS</td><td>2021</td><td>GB</td><td>OCT</td><td>3D</td><td>Eye</td><td>N/A</td><td>N/A</td></tr><tr><td>[196]</td><td>NIH Chest Xray</td><td>NAS</td><td>2024</td><td>GB</td><td>X-Ray</td><td>2D</td><td>Lungs N/A</td><td>N/A</td><td>N/A</td></tr><tr><td colspan="10" style="text-align:center; font-weight:bold;">Evolutionary Algorithms</td></tr><tr><td>[171]</td><td>MedMNIST</td><td>NAS</td><td>2024</td><td>EA</td><td>Multi-Modalities</td><td>2D</td><td>Multi-Organ</td><td>DGX A100 GPU</td><td>0.1</td></tr><tr><td>[72]</td><td>Clean-CC-CCII, MosMedData, COVID-Ctset</td><td>NAS</td><td>2021</td><td>EA</td><td>CT</td><td>3D</td><td>Chest</td><td>4 Nvidia Tesla V100 GPU</td><td>1.3</td></tr><tr><td>[6]</td><td>MedMNIST</td><td>NAS</td><td>2023</td><td>EA</td><td>Multiple Modalities</td><td>2D</td><td>Multi-Organ</td><td>RTX 8000</td><td>18</td></tr><tr><td>[141]</td><td>Chexpert</td><td>NAS</td><td>2020</td><td>EA</td><td>X-ray</td><td>2D</td><td>Chest</td><td>NVIDIA 8 Ryzen 2700 GeForce GTX1080</td><td>28</td></tr><tr><td>[36]</td><td>Privately collected dataset</td><td>NAS</td><td>2022</td><td>EA</td><td>X-Ray</td><td>2D</td><td>Chest</td><td>N/A</td><td>3.4</td></tr><tr><td>[92]</td><td>ISIC Skin Leison</td><td>NAS</td><td>2020</td><td>EA</td><td>Skin Images</td><td>2D</td><td>N/A</td><td>N/A</td><td>18</td></tr><tr><td>[93]</td><td>MALIGNANT MELANOMA DETECTION</td><td>NAS</td><td>2019</td><td>EA</td><td>Skin Images</td><td>2D</td><td>Skin</td><td>N/A</td><td>31.2</td></tr><tr><td>[117]</td><td>Sperm images private dataset</td><td>NAS</td><td>2023</td><td>EA</td><td>Sperm microscopic images</td><td>2D</td><td>Sperm</td><td>N/A</td><td>237</td></tr><tr><td>[105]</td><td>LC25000</td><td>NAS</td><td>2022</td><td>EA</td><td>Histopathology</td><td>2D</td><td>Lung, Colon</td><td>N/A</td><td>N/A</td></tr><tr><td>[59]</td><td>Covid-CT dataset</td><td>NAS</td><td>2021</td><td>EA</td><td>CT</td><td>2D</td><td>Chest</td><td>2 GTX2080Ti</td><td>N/A</td></tr><tr><td>[218]</td><td>Private dataset</td><td>NAS</td><td>2021</td><td>EA</td><td>Edoscopy</td><td>2D</td><td>Stomach</td><td>2 Nvidia GeForce GTX 1080Ti</td><td>N/A</td></tr><tr><td>[125]</td><td>Bach, BreakHIS</td><td>NAS</td><td>2021</td><td>EA</td><td>Histopathology</td><td>2D</td><td>Breast</td><td>N/A</td><td>N/A</td></tr></tbody></table>
<table><tbody><tr><td>[187]</td><td>MedMNIST</td><td>NAS</td><td>2022</td><td>EA</td><td>Multiple Modalities</td><td>2D</td><td>Multi- Organs</td><td>Nvidia Titan RTX</td><td>N/A</td></tr><tr><td>[36]</td><td>Custom Dataset gathered</td><td>NAS</td><td>2023</td><td>EA</td><td>X-ray</td><td>2D</td><td>Spine</td><td>N/A</td><td>N/A</td></tr><tr><td>[33]</td><td>BRATS</td><td>NAS</td><td>2022</td><td>EA</td><td>MRI</td><td>2D</td><td></td><td>N/A</td><td>N/A</td></tr><tr><td>[39]</td><td>ABIDE</td><td>NAS</td><td>2020</td><td>EA</td><td>fMRI</td><td>3D</td><td>Brain</td><td>Quadro K600</td><td>N/A</td></tr><tr><td>[157]</td><td>AMD, ABIDE</td><td>AExAI</td><td>2020</td><td>EA</td><td>MRI</td><td>3D</td><td>Brain</td><td>N/A</td><td>N/A</td></tr><tr><td>[38]</td><td>MedMNIST</td><td>NAS</td><td>2021</td><td>EA</td><td>Multiple Modalities</td><td>2D/3D</td><td>All organs</td><td>N/A</td><td>N/A</td></tr><tr><td>[52]</td><td>ISIC, Chest X-Ray</td><td>Pruning</td><td>2021</td><td>EA</td><td>X-Ray, Dermatology</td><td>2D</td><td>Chest, Skin</td><td>N/A</td><td>N/A</td></tr><tr><td>[51]</td><td>ISIC, Chest X-Ray</td><td>Pruning</td><td>2021</td><td>EA</td><td>X-Ray, Dermatology</td><td>2D</td><td>Chest, Skin</td><td>N/A</td><td>N/A</td></tr><tr><td>[96]</td><td>CheXNet</td><td>HPO + NAS</td><td>2019</td><td>EA</td><td>X-ray</td><td>2D</td><td>Chest</td><td>N/A</td><td>N/A</td></tr><tr><td>[123]</td><td>Covid19-Xrays, HAM10000</td><td>HPO</td><td>2021</td><td>EA</td><td>Xray, Skin Images</td><td>2D</td><td>Skin, Chest</td><td>N/A</td><td>N/A</td></tr><tr><td>[211]</td><td>LIDC-IDRI</td><td>HPO</td><td>2021</td><td>EA</td><td>CT</td><td>2D</td><td>Lungs</td><td>N/A</td><td>N/A</td></tr><tr><td>[112]</td><td>Chest Xray14</td><td>NAS</td><td>2021</td><td>EA</td><td>Multiple Modalities</td><td></td><td>Chest</td><td>8 Nvidia 2080Ti GPU</td><td>N/A</td></tr><tr><td colspan="10" style="text-align: center; font-weight: bold;">Bayesian Optimization</td></tr><tr><td>[210]</td><td>LIDC-IDRI</td><td>HPO</td><td>2019</td><td>BO</td><td>X-Ray</td><td>2D</td><td>Lungs</td><td>Nvidia Quadro P5000</td><td>N/A</td></tr><tr><td>[22]</td><td>Nerthus, Kvasir</td><td>HPO</td><td>2021</td><td>BO</td><td>Endoscopy Images</td><td>2D</td><td>Stomach</td><td>1 Nvidia Tesla K40c 2 GTX TITAN X</td><td>N/A</td></tr><tr><td>[12]</td><td>Covid-19 Dataset Xrays</td><td>HPO</td><td>2022</td><td>BO</td><td>CT</td><td>2D</td><td>Chest</td><td>N/A</td><td>N/A</td></tr><tr><td colspan="10" style="text-align: center; font-weight: bold;">Analysis/Comparison of AutoML tools</td></tr><tr><td>[167]</td><td>BUSI dataset</td><td>AutoML Tools used</td><td>2021</td><td></td><td>Ultrasound</td><td>2D</td><td>Breast</td><td>N/A</td><td>N/A</td></tr><tr><td>[90]</td><td>Kermany OCT, Waterloo OCT, Messidor 2</td><td>AutoML Tools used</td><td>2021</td><td></td><td>OCT</td><td>2D</td><td>Eye</td><td>N/A</td><td>N/A</td></tr><tr><td>[156]</td><td>CRC_DX, CDC_KR, STAD</td><td>AutoML Tools used</td><td>2022</td><td></td><td>Histopathology</td><td>2D</td><td>Prostate</td><td>NVIDIA GeForce GTX 1080 Ti</td><td>N/A</td></tr><tr><td colspan="10" style="text-align: center; font-weight: bold;">Other Approaches</td></tr><tr><td>[55]</td><td>ISIC 2018, OARs MICCAI 2015</td><td>ADA</td><td>2021</td><td>RAT</td><td>Multiple Modalities</td><td>2D/3D</td><td>Skin, Head, Neck</td><td>NVIDIA RTX8000 GPUs</td><td>6.2</td></tr><tr><td>[85]</td><td>LIDC-IDRI, LUNA 156</td><td>NAS</td><td>2021</td><td>POP</td><td>CT</td><td>3D</td><td>Lungs</td><td>GTX 1080Ti</td><td>8</td></tr><tr><td>[71]</td><td>CC-CCII</td><td>NAS</td><td>2020</td><td>RSA</td><td>CT</td><td>2D/3D</td><td>Chest</td><td>4 Nvidia Tesla V100 GPU</td><td>2</td></tr><tr><td>[124]</td><td>ISIC 2019</td><td>NAS</td><td>2021</td><td>SMBO + TPE</td><td>Camera Images</td><td>2D</td><td>Skin</td><td>N/A</td><td>N/A</td></tr><tr><td>[98]</td><td>LIDC-IDRI</td><td>HPO</td><td>2021</td><td>BO + LS</td><td>X-Ray</td><td>2D</td><td>Lungs</td><td>Nvidia Titan X</td><td>N/A</td></tr></tbody></table>
<table><caption>Table 5: Comparison of multiple AutoML based medical image classification approaches with respect to different settings, GPU used and searching time. MedMNIST in datasets used refers to datasets used from MedMNIST benchmark. The method which consumes short searching time is highlighted with green color.</caption><tbody><tr><td>[17]</td><td>Private Dataset</td><td>Model Selection</td><td>2017</td><td>RS + AGP</td><td>MRI</td><td>3D</td><td>Whole Body</td><td>N/A</td><td>N/A</td></tr><tr><td>[48]</td><td>CBIS-DDSM</td><td>HPO</td><td>2021</td><td>GS + BO</td><td>Mammograms</td><td>2D</td><td>Breast</td><td>RTX 2080 Ti</td><td>N/A</td></tr><tr><td>[62]</td><td>EyePACS</td><td>NAS</td><td>2020</td><td>RNN</td><td>OCT</td><td>2D</td><td>Eye</td><td>N/A</td><td>N/A</td></tr><tr><td>[207]</td><td>IDC Breast Dataset</td><td>AutoML Tools used</td><td>2020</td><td>RL</td><td>Histopathology</td><td>2D</td><td>Breast</td><td>8 NVIDIA Tesla® V100</td><td>N/A</td></tr><tr><td>[82]</td><td>Private dataset</td><td>AutoML Tools used</td><td>2021</td><td>EA + BO</td><td>X-Ray</td><td>2D</td><td>Chest</td><td>N/A</td><td>N/A</td></tr><tr><td>[192]</td><td>Multiple pre-processed datasets</td><td>AutoML Benchmark</td><td>2021</td><td>EA + BO</td><td>X-Ray, OCT, Ultrasound, CT, Electron Microscope</td><td>2D/3D</td><td>Multi-Organs</td><td>N/A</td><td>0.009</td></tr><tr><td colspan="10" style="text-align: center; font-weight: bold;">Zero Cost Approach</td></tr><tr><td>[209]</td><td>MedMNIST</td><td>NAS</td><td>2023</td><td>EA</td><td>Multiple Modalities</td><td>2D</td><td>Multi-Organs</td><td>N/A</td><td>0.006</td></tr></tbody></table>
The encoder path captures the context of import information and downsizes the input into a compressed vector using pooling layers and convolutional filters. The decoder path reconstructs the compressed vector for precise localization using transposed convolutions. Early 3D architectures for medical image segmentation include U-Net and V-Net, with later modifications such as U-Net++, NN-Unet, TransUNET, Attention-based U-Net, and Multi-level U-Nets [159].

Designing the encoder and decoder of U-Net based FCN architecture, choosing the depth and choices of skip-connections between different layers is a challenging problem. Multiple skip-connections may result in better segmentation accuracy as reported in U-Net++ [219]. However, the choices of skip-connections and depth size have different impacts on different tasks [159]. Most of these approaches use One-Shot NAS as this approach is resource-efficient. It searches for sub-nets from a super-net trained on the datasets. One of the early NAS architectures for medical image segmentation includes NAS-UNET and V-NAS. The first study which automatically designed U-Net using NAS for medical image segmentation was published in 2017 by Wang et al. [175]. They modified the DARTS approach with encoder-decoder-based search space medical image segmentation as shown in Figure 14. Moreover, V-NAS proposed by Zhu et al. also uses differentiable approach and its search space consists of 2D, 3D, and pseudo-3D convolutions [220]. Their baseline architecture uses encoder-decoder block-based representation.

Isensee et al. proposed nnUNET, a self-adapting framework for medical image segmentation. It first analyzes the data and configures an entire end-to-end pipeline for a given dataset without any expert knowledge or extensive computing resources to try and run each configuration. It uses rule-based optimization to choose the best set of hyperparameters and build an ensemble of multiple 2D,3D, and Cascade networks. They have performed experiments on MSD datasets and achieved state-of-the-art results on all the datasets [80]. Following the contexte, MONAI, which is an open-access framework for building medical image applications, proposed Auto3dSeg, that builds an end-to-end pipeline for medical image segmentation by pre-processing, automatically choosing the optimal set of parameters, training, and evaluating the model. This algorithm also provides the option to use the DINTS NAS algorithm to automatically search an architecture with an end-to-end pipeline for segmentation [160]. Their proposed approach ranked first in several medical image segmentation competitions. To facilitate comprehension, we have broadly categorized these segmentation approaches based on their search strategies. Several evolutionary approaches have been proposed to automatically design architectures for medical image segmentation. These approaches consist of single or multiple objectives that use error rate or Dice Similarity Coefficient (DSC) as the fitness function for a single objective and network size, F1-Score, latency, and number of FLOPS as multiple objectives. Most studies have used datasets from

Medical Segmentation Decathlon (MSD) to evaluate and
compare the performance of the proposed approach.

4.2.1. Single Objective Evolutionary approaches

Several metaheuristic algorithms have been employed to solve medical image segmentation problems automatically. In [174], a genetic U-Net approach is proposed for retinal vessel segmentation. They used a block-based encoding scheme for representation, and a difference-guided crossover operator is used to maintain diversity among individuals. Moreover, Mahsa et al. proposed an approach for optimizing U-Net architecture parameters for retinal vessel segmentation, finding smaller models with higher accuracy [115]. Their proposed approach is quite effective but their searching time is relatively high.

Moreover, Khouy et. al proposed to use GA to optimize the U-Net architecture while minimizing the complexity of the architectures parameters [88]. They have performed experiments on three different datasets. Similarly, Hassanzadeh et al. proposed multiple GA-based approaches for medical image segmentation. They used six different datasets from the MSD benchmark, which consist of different organs and modalities. Their search space for the first approach consists of several filters in the convolution layer, skip connections, activation functions, dropout layers, optimizers, learning rates, and the number of layers in each block [66]. In the second approach, they extended the search space with dense layers and residual connections [65], and their third approach uses attention-layers [64]. The same authors proposed ensembles of evolutionary searched architectures, where they combine multiple searched architectures to improve the overall performance [68]. Although these approaches are advantageous in terms of performance, the important search time remains a serious issue. To overcome this problem, several studies have incorporated different performance estimation strategies.

Similarly, other meta-heuristics have been considered. In [91], the authors proposed an opposite differential evolution (ODE) algorithm to search for micro and macro search spaces for U-Net and Attention U-Net segmentation architectures. The opposite-based DE considers the both current solution and their opposites in the search space during the optimization process to potentially find better solutions. They have performed experiments on two different datasets and achieved better performance in comparison with existing studies. Similarly, Yu et al. used the DE algorithm to search for the U-Net architecture structure for medical image segmentation [201]. They have adopted a variable length strategy to represent the individuals, allowing the algorithm to explore various solutions. The comparison of two different medical datasets demonstrated the effectiveness of the proposed approach. Rajesh et al. used the Chameleon Swarm Algorithm (CSA) algorithm to search for an architecture for 3D medical image segmentation [146]. CSA algorithm is proposed to solve unconstrained optimization problems, which simulate the dynamic behavior of the chameleon foraging method to design an optimization algorithm. They
<figure><img src="image_19.png" /><figcaption>**Figure 14:** U-Net, like the backbone architectures NAS-Unet used in this [175], uses a curved rectangle to represent the need to search for cell architectures. The flow of feature maps is represented by green arrows. The gray arrow represents the skip connections between the UpSC and DownSC blocks. The NAS-Unet cell architecture is shown, where red lines indicate the down or up operation, blue arrows indicate the normal operation, and green arrows represent the concatenate operation.</figcaption></figure>

defined a search space consisting of different blocks containing different layer combinations and different activation functions, loss functions and optimizers.

Furthermore, Liu et al. proposed an approach for searching 3D FCN-based architectures for brain tumor segmentation [103]. They defined the search space as a directed graph and used GA to search for a potential architecture. Moreover, a NAS approach for searching two-level nested U-shaped architecture is proposed in [163]. Their proposed approach searches a two-level nested U-shaped architecture that extracts features from two levels for more refined features. Similarly, other meta-heuristics are also explored for this problem. In [145], Rajesh et al. proposed a Binary Teaching-Learning-Based Optimization (BTLO) approach for designing a U-Net-shaped architecture to search for blocks and attention types. Although these approaches are quite efficient but didn't focus on the number of parameters. Furthermore, performance estimation strategies were not used by these approaches to reduce to search time.

GE approaches have also recently been adopted for medical image segmentation and are very effective in finding suitable architectures. In [116], the authors proposed to use grammatical evolution for designing U-Net architecture, in which they formulate grammar to evolve the U-Net architecture and its hyperparameters. They used spatial attention U-Net, which utilizes the Drop-Block regularization method to prevent overfitting. The proposed approach finds the hyperparameters (kernel size, pooling layer, block size, padding, layer size) and the number of parameters. Their proposed approach outperforms existing segmentation approaches on the DRIVE eye retina dataset. Similar studies are also proposed using GA for retinal vessel segmentation [114], [76] and [134]. In [54], The authors proposed a tree-based GP representation to design the topology of U-Net architecture for medical image segmentation. This approach enables complex connectivity and various operations. Experiments on multiple datasets showed that this proposed approach surpassed existing NAS methods in terms of results compared
to existing NAS methods. For fitness evaluation, they used a weighted sum method to combine multiple objectives: DSC and Complexity Factor (CF).

Unlike metaheuristics, grammar-based approaches do not rely on predefined solution representations. Instead, GE offers a dynamic and flexible framework by leveraging grammatical descriptions of the problem spaces. Grammar-based representation offers flexibility in the architecture design. By defining the grammar rules, various architectural variations can be explored. Thus, grammar-based approaches for medical images focus essentially on design principles. Besides, they avoid exhaustive search by only focusing on promising architectural patterns, exhibiting a faster convergence. Nevertheless, defining a grammar that must capture relevant architecture components is computationally expensive. Besides, poorly designed grammar may lead to sub-optimal architectures.

Moreover, a data-variation-aware medical image segmentation approach uses a multi-path U-Net trained on specific data subsets, with the best partitioning into subsets automatically determined by an evolutionary algorithm. Experiments on multiple datasets demonstrate the effectiveness of the proposed approach [45]. This approach is capable of positively handling variations in data by training multiple deep learning algorithms increasing accuracy and diversity in segmentation. However, its complex nature, its high computational time and the risk of over-fitting to specific subsets emerges as a serious issue.

Automatic model selection techniques are also proposed for medical image segmentation. Automatic model selection, or ensemble search or automated model selection, is a technique used to automatically select the top-performing machine learning model or algorithms for a given task, dataset, or issue. Several studies use evolutionary techniques to search for an optimal set of ensembles of deep-learning models for a given medical imaging task. Dang et al. proposed to search for a weighted ensemble for segmentation models based on a deep learning algorithm where each model's predictions are combined using weighted summation. They used Comprehensive Learning Particle Swarm Optimization (CLPSO) to search for the optimal threshold for each segmentation model. Experiments on three datasets outperform existing algorithms [41]. In a similar study, they proposed to find an ensemble of medical image segmentation models using each model's prediction confidence and threshold. Each model's combined confidence and threshold are searched using the CLPSO algorithm. Moreover, surrogate models effectively reduce the search time for a set of architectures using PSO for medical image segmentation [40].

### 4.2.2. Multi Objective Evolutionary approaches

A large number of studies have proposed multi-objective approaches. The benefit of multi-objective approaches is that they focus on finding an efficient architecture with fewer parameters/ computational complexity.

Calisto et al. introduced a multi-objective based adaptive Res U-Net architecture for segmentation in medical images based on the MOEA/D algorithm. The latter increases the model's accuracy while reducing the number of parameters. Their proposed solution performs HPO by optimizing the learning rates, dropout probability, and kernel sizes. The comparison of their approach with BO with GP as a surrogate function shows that the proposed approach outperforms GP [14]. To overcome this issue, the approach is further developed to reduce the search time by using the RF regression model as a surrogate model [25]. The benefit of using their approach is that it searches for an efficient model, requiring less search time thanks to surrogate models. However, the surrogate model's training data impacts the prediction quality. The training data requires significant time to evaluate multiple architectures, a disadvantage of their approach. To overcome this, studies have utilised a weight-sharing approach with surrogate models to increase the numbers of evaluated architectures for surrogate models.

Furthermore, Calisto et al. proposed an ensemble of 2D-3D FCN architectures that optimize the model performance and reduce the number of parameters. They apply the mutation vector independently to each component of the hyper-parameter vector with a probability that decreases monotonically. The two objectives are the number of trainable parameters and the expected segmentation error. Their proposed approach achieves competitive performance on PROMISE and MICCAI ACDC challenge [24]. In a similar study, the authors proposed using 2D and 3D FCN-based architectures as an ensemble to solve the medical image segmentation problem. Their idea is to use 2D FCN to extract intra-slice information and 3D FCN to exploit volumetric information. They adopt both architectures using a multi-objective evolutionary approach, which finds an optimal architecture with less segmentation error and fewer parameters [26]. They proposed an optimization algorithm named MEA, the MOEA/D algorithm, and a penalty-based boundary intersection approach to approximate the non-dominated Pareto frontier. The MEA algorithm defines the architecture by minimizing two objective functions: the segmentation error and the number of parameters. The experimentation results reveal that their approach increases the segmentation accuracy and model ability while preserving a reasonable computational cost.

Furthermore, Lu et al. proposed an effective method for 3D cardiac cine MRI segmentation [108]. They have used a two-level search space to be optimized, having both cell-based and backbone-based elements. Moreover, a latency constraint is incorporated during the search process. The latency of the architecture is reduced if it fails to satisfy the criteria, and the search is re-performed. This aims to obtain the Pareto front between the accuracy and network latency metrics. They used a multi-objective GA for NAS to search for an optimal architecture. Although their proposed approach is quite good both in terms of performance and latency but takes lot of time to search for an architecture due to meeting the latency criteria.
Multi-objective algorithms have also been proposed to perform both the segmentation and classification of esophageal lesions. Their method uses a One-Shot NAS approach in which, a SuperNet is trained. Then, the weight-sharing approach is used during the evolutionary search to find the best-performing architecture [218].

Recent studies combined multiple performance estimation strategies, such as weight-sharing mechanisms and surrogate models, to reduce the search time of the NAS approach for medical image segmentation [200]. Moreover, they also proposed to use a convolution transformer-based search space and a multi-objective algorithm to optimize both architecture performance and the model's complexity.

### 4.2.3. Gradient based approaches

Studies have proposed different gradient-based approaches for medical image segmentation. One important reason behind adapting gradient-based techniques for medical image segmentation is that they are highly efficient. A famous gradient-based NAS approach called DARTS is employed and extended for medical image segmentation tasks. It is a one-shot method to select sub-networks from a Super-Net while sharing weights.

Numerous methods for medical image segmentation have been developed, utilizing gradient-based NAS approaches. In [175], a DARTS-based approach is used for searching cells in 2D U-Net-based encoder-decoder architecture. Each cell consists of three primitive operations. They evaluated their proposed approach into multiple datasets (Promise12, CHOAS, and ultrasound nerve datasets), which performed better than baseline architectures.

Moreover, A differentiable NAS approach is proposed to optimize the encoder, decoder, expansion, and reduction cells of 3D FCN baseline architecture. They proposed a scalable approach for architecture search. It uses a novel continuous approximation using Gumbel-softmax sampling on discrete variables, making it possible to use stochastic gradient descent for bi-level optimization. The proposed approach enables great scalability for solving large search space while maintaining less computational cost [89]. A flexible multipath topology with high search efficiency within limited GPU budget and an optimal cell-based structure are also proposed for searching lightweight architectures [13] [74] [31].

To overcome this, a coarse-to-fine NAS approach searches both micro and macro-structures of architectures through a two-stage optimization problem [203]. This approach searches for both micro and macro structures, making it fully automatic. However, the two-stage search process can be computationally expensive and time-consuming. Similarly, an adaptive depth and width U-Net architecture for medical image segmentation is proposed. They define the search space in which NAS can adaptively adjust the depth and width of U-Net in different medical image segmentation tasks.

Their proposed approach reduces the computational costs and achieves state-of-the-art results [77]. But in comparison with nnUNet, the proposed approach is not significant. Similarly, In [27], the authors proposed AutoDenseUNet architecture for accurate mass segmentation in 3D Automated Breast Ultrasound (ABUS) images, aiming to improve early detection of breast cancer. Using dense blocks and skip connections, it outperforms deep convolutional networks in segmenting ABUS images, addressing challenges like low contrast and high noise.

Wang et al. extended the BiO-Net architecture, which uses forward and backward skip connections between the encoder and decoder blocks. Bix-Net searches for bidirectional multi-scale skip connections between encoder and decoder, that the performance of model increases and the number of model size shrinks [170]. This approach uses bi-directional mechanism to enhance feature fusion. This latter could lead to overfitting if the network excessively adapts to the training data peculiarities rather than learning generalizable features.

In [43], a NAS-based approach is proposed to design an architecture for adversarial networks automatically. They proposed to design a discriminator in a GAN-based architecture, for which they used continuous relaxation with gradient-based optimization. This approach is computationally expensive due to the evaluation of the discriminator. Multi-task approaches were also proposed. Qian et al. adopted a hybrid approach for echinococcosis classification and ovary segmentation using ultrasound images. First, they used a pre-trained bottleneck, and the remaining cells were searched using a gradient-based approach. This approach combines both NAS and expert knowledge. They performed experiments on two different datasets [135]. Moreover, In [204], the authors proposed a hybrid CNN-Transformer architecture with single path NAS for medical image segmentation. They used a key-sampling transformer in the backbone, which considers contextual and long-pixel information. Morevoer, Liu et al. proposed a transformer-based NAS approach for multi-task learning [104]. They jointly solve the brain tumor segmentation and classification tasks.

Recently, transformer layers have been combined with CNN ones to search FCN architectures for medical image segmentation. Xu et al. proposed searching an encoder-decoder architecture with CNN and Transformers for medical image segmentation. They construct a lightweight transformer block using local-global attention. Then, an efficient resource-constrained strategy is used to balance the number of parameters and the performance to search for efficient architecture with few FLOPs [183]. In [152], the authors utilize FL with AutoML to search for neural architectures using NAS. Then, they adopt FL to average the predictions from multiple servers to improve data security. Furthermore, an adoption scheme is used to have a personalized model architecture for every client. They performed experiments on multiple prostate MRI datasets.
<table><thead><tr><th>Reference</th><th>Dataset</th><th>Type of AutoML problem</th><th>Year</th><th>Modality</th><th>Type of data</th><th>Organ</th><th>GPU Used</th><th>Searching time (In hours)</th></tr></thead><tbody><tr><td colspan="9" style="text-align:center; font-weight:bold;">Reinforcement Learning approaches</td></tr><tr><td>[185]</td><td>EM stacks challenge - ISBI 2012</td><td>NAS</td><td>2020</td><td>Microscopy</td><td>2D</td><td>Brain</td><td>GeForce RTX 2080</td><td>8.45</td></tr><tr><td>[56]</td><td>AREDS 2 OCT Dataset</td><td>NAS</td><td>2020</td><td>OCT</td><td>2D</td><td>Eyes</td><td>NVIDIA GTX 1080 Ti</td><td>12</td></tr><tr><td>[13]</td><td>BRATS 2016 AND 2017, MSD</td><td>NAS</td><td>2019</td><td>MRI,CT</td><td>3D</td><td>Brain, Heart, Prostate</td><td>RTX 2080Ti</td><td>33.36</td></tr><tr><td>[42]</td><td>Private Collected dataset</td><td>NAS</td><td>2020</td><td>OCT</td><td>2D</td><td>Eye</td><td>12 GB NVIDIA GeForce RTX 2080 Ti GPU</td><td>24</td></tr><tr><td>[190]</td><td>MSD and BRATS 2016.2018</td><td>NAS + HPO</td><td>2020</td><td>CT/MRI</td><td>3D</td><td>Heart, Lungs, Pancreas, Tumors, Spleen</td><td>50 NVIDIA V100 GPUs</td><td>48</td></tr><tr><td colspan="9" style="text-align:center; font-weight:bold;">Gradient based Approach</td></tr><tr><td>[204]</td><td>CVC-CLINICDB, CHAOS228 CT, ISIC-2018</td><td>NAS</td><td>2023</td><td>CT,MRI</td><td>3D</td><td>Stomach, Lungs</td><td>Tesla P100-PCIE-16GB, NVIDIA RTX A4000-16GB</td><td>2</td></tr><tr><td>[135]</td><td>US Images dataset</td><td>NAS</td><td>2022</td><td>Ultrasound</td><td>2D</td><td>Liver</td><td>Titan X GPU with 12GB RAM</td><td>10</td></tr><tr><td>[220]</td><td>MSD</td><td>NAS</td><td>2019</td><td>CT</td><td>3D</td><td>Lungs, Pancreas</td><td>V100 GPU</td><td>30</td></tr><tr><td>[175]</td><td>MSD, ULTRASOUND NERVE</td><td>NAS</td><td>2019</td><td></td><td>2D</td><td>Liver, Abdoman, Prostate, Nerves</td><td>12GB Titan pascal GPU</td><td>39</td></tr><tr><td>[188]</td><td>Sliver07, Promise12,chaos</td><td>NAS</td><td>2020</td><td>CT/MRI</td><td>3D</td><td>Liver, Kidney, Spleen, Prostate</td><td>GTX 1080Ti GPU</td><td>48</td></tr><tr><td>[57]</td><td>In House OCT Dataset</td><td>NAS</td><td>2020</td><td>Optical coherence tomography (OCT)</td><td>2D</td><td>Eye</td><td>2 GeForce RTX 2080 Ti GPU</td><td>24</td></tr><tr><td>[77]</td><td>MSD</td><td>NAS</td><td>2022</td><td></td><td>3D</td><td>Multi- Organs</td><td>NVIDIA V100 GPU</td><td>48</td></tr><tr><td>[83]</td><td>MSID, ISIS, CHOAS, BRATS 2016 and 2017</td><td>NAS</td><td>2020</td><td>MRI/CT</td><td>2D/3D</td><td>Brain, Heart, Prostate, Skin</td><td>2 TitanXP GPU</td><td>36</td></tr><tr><td>[89]</td><td>BRATS 2016 and 2017,MSD</td><td>NAS</td><td>2019</td><td>MRI, CT</td><td>3D</td><td>Brain Tumor, Heart, Lungs</td><td>4 V100 GPUs</td><td>24</td></tr></tbody></table>
<table><tr><td>[43]</td><td>Japanese Society of Radiological Technology Lung Nodule Dataset</td><td>NAS</td><td>2019</td><td>X-Ray</td><td>2D</td><td>Lungs, Heart</td><td>2 GTX Titan X GPUs</td><td>72</td></tr><tr><td>[74]</td><td>MSD and BRATS 2016.2018</td><td>NAS</td><td>2021</td><td>CT/MRI</td><td>3D</td><td>Liver, Kidney, Spleen, Prostate, Heart, Brain, Lungs, Hip-pocam-pus, colon, Hepatic Vessels</td><td>8 Gpus</td><td>139.2</td></tr><tr><td>[119]</td><td>ACDC MICCAI 2017</td><td>NAS</td><td>2018</td><td>MRI</td><td>4D</td><td>Heart</td><td>15 GPUs (Titan X)</td><td>240</td></tr><tr><td>[108]</td><td>ACDC Dataset</td><td>NAS</td><td>2023</td><td>MRI</td><td>3D</td><td>Cardiac</td><td>NVIDIA Tesla P100 GPU</td><td>N/A</td></tr><tr><td>[152]</td><td>MSD-Prostate1, PROMISE12, NCI-ISBI133, ProstateX4</td><td>NAS</td><td>2021</td><td>MRI</td><td>3D</td><td>Prostate</td><td>NVIDIA V100 GPU</td><td>N/A</td></tr><tr><td>[61]</td><td>MICCAI 2015 Dataset and Custom H&amp;N dataset</td><td>NAS</td><td>2020</td><td>Computed Tomography (CT)</td><td>3D</td><td>Head, Neck</td><td>NVIDIA Quadro RTX 8000</td><td>N/A</td></tr><tr><td>[168]</td><td>BraTS 2019</td><td>NAS</td><td>2020</td><td>MRI</td><td>3D</td><td>Brain</td><td>GTX1080Ti GPU</td><td>N/A</td></tr><tr><td>[183]</td><td>Synapse multi-organ, Chaos and ACDC</td><td>NAS</td><td>2021</td><td>MRI, CT</td><td>3D</td><td>Brain, Liver</td><td></td><td>N/A</td></tr><tr><td>[31]</td><td>CHAOS, PROMISE-12</td><td>NAS</td><td>2022</td><td>MRI, CT</td><td>3D</td><td>Prostate, Abdomen</td><td></td><td>N/A</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">Evolutionary Approaches</td></tr><tr><td>[201]</td><td>CHOAS, BUSI</td><td>NAS</td><td>2023</td><td>CT</td><td>3D</td><td>Liver, Kidney, Spleen</td><td>2 Nvidia TITAN XP</td><td>528</td></tr><tr><td>[146]</td><td>CHOAS, ACDC.SPLEEN</td><td>NAS</td><td>2024</td><td>CT, MRI</td><td>3D</td><td>Spleen, Heart</td><td>Quadro P5000</td><td></td></tr><tr><td>[54]</td><td>ISIC, DRIVE, COVID, PROMISE, HEART, HIPPOCAMPUS</td><td>NAS</td><td>2023</td><td>OCT, CT, MRI</td><td>3D</td><td>Eye, Heart, Lungs</td><td>VOLTA V100</td><td></td></tr><tr><td>[91]</td><td>vesseINN, KUVESG</td><td>NAS</td><td>2023</td><td>Cerebrovascular Imaging</td><td>2D</td><td>Brain</td><td>Ryzen 5600X processor</td><td></td></tr><tr><td>[88]</td><td>DSB 2018</td><td>NAS</td><td>2023</td><td>Nuclei Cell Images</td><td>2D</td><td>Liver</td><td>Nvidia Tesla P100 GPU</td><td></td></tr><tr><td>[200]</td><td>ISIC, MoNUSEG</td><td>NAS</td><td>2023</td><td>Skin, Histopathology</td><td>2D</td><td>Multi-Organ, Skin</td><td>RTX 2080Ti</td><td></td></tr><tr><td>[31]</td><td>CHAOS, PROMISE-12</td><td>NAS</td><td>2022</td><td>MRI, CT</td><td>3D</td><td>Prostate, Abdomen</td><td></td><td>N/A</td></tr><tr><td>[76]</td><td>DRIVE</td><td>NAS</td><td>2021</td><td>IRIS</td><td>2D</td><td>Eye</td><td>2 GPUs; Nvidia Tesla V100-PCIIE</td><td>3</td></tr><tr><td>[218]</td><td>Privately Collected esophageal Images dataset</td><td>NAS</td><td>2021</td><td>Edoscopy</td><td>2D</td><td>Stomach</td><td>Tesla P100</td><td>13</td></tr><tr><td>[65]</td><td>Promise12, CHOAS, SILVER07, Brats19</td><td>NAS</td><td>2020</td><td>MRI, CT</td><td>3D</td><td>Prostate, Spleen, Abdominal, Brain</td><td>Nvidia GPU</td><td>60-92</td></tr></table>
<table><tr><td>[28]</td><td>Prostate, Spleen</td><td>NAS</td><td>2023</td><td>MRI</td><td>3D</td><td>Prostate, Spleen</td><td>N/A</td><td>90</td></tr><tr><td>[14]</td><td>PROMISE12 Grand Challenge, MICCAI ACDC challenge</td><td>HPO</td><td>2020</td><td>MRI</td><td>3D</td><td>Prostate, Heart</td><td>NVIDIA GeForce GTX 1080 GPU</td><td>60-110</td></tr><tr><td>[67]</td><td>MSD</td><td>NAS</td><td>2021</td><td>MRI, CT</td><td>3D</td><td>Spleen, Prostate, Hippocampus, Liver, Heart</td><td>2D Nvidia Tesla Volta V100 GPU+ 3D (2Nvidia Tesla Volta V100)</td><td>120-228 (2D) 1152 - 2304 (3D)</td></tr><tr><td>[103]</td><td>BRATS</td><td>NAS</td><td>2023</td><td>MRI</td><td>3D</td><td>Brain</td><td>NVIDIA RTX3090 24GB</td><td>126.3</td></tr><tr><td>[170]</td><td>MoNuSeg, TNBC, CHOAS</td><td>NAS</td><td>2021</td><td>Microscopy, MRI, CT</td><td>2D</td><td>Multi-Organ, Abdominal</td><td>N/A</td><td>139.2</td></tr><tr><td>[203]</td><td>BRATS 2016 and 2017, MSD</td><td>NAS</td><td>2020</td><td></td><td>2D/3D</td><td>Brain, Liver, Pancreas, Prostate, Lung, Heart, Hippocampus, HepaticVessel, Spleen, Colon</td><td>64 NVIDIA V100 GPUs</td><td>120</td></tr><tr><td>[49]</td><td>DRIVE, STARE and CHASE_DB1</td><td>NAS</td><td>2020</td><td>Retinal imaging/Fundus Photography</td><td>2D</td><td>Eye</td><td>4 NVIDIA TITAN Xp GPUs</td><td>N/A</td></tr><tr><td>[24]</td><td>PROMISE12 Grand Challenge, MICCAI ACDC challenge</td><td>NAS + HPO</td><td>2020</td><td>MRI</td><td>3D</td><td>Prostate, Heart</td><td>1 GTX 1080 GPU</td><td>326.4</td></tr><tr><td>[63]</td><td>MSD</td><td>NAS</td><td>2020</td><td>CT/MRI</td><td>3D</td><td>Prostate; Brain, Lungs, Chest, Abdomen, Hippocampus</td><td>N/A</td><td>288 - 912</td></tr><tr><td>[66]</td><td>PROMISE12 dataset</td><td>NAS</td><td>2020</td><td>MRI</td><td>3D</td><td>Prostate</td><td>4 Nvidia GPUs</td><td>300-912</td></tr><tr><td>[174]</td><td>DRIVE, CHASE_DB1, STARE, HRF</td><td>NAS</td><td>2020</td><td></td><td>2D</td><td>Eye</td><td>2 NVIDIA TITAN RTX GPU</td><td>N/A</td></tr><tr><td>[25]</td><td>PROMISE12 Hippocampus, ACDC challenge</td><td>NAS</td><td>2021</td><td></td><td>3D</td><td>Prostate, Hippocampus, Heart</td><td>8-GB GeForce GTX 1070 Ti GPU</td><td>75</td></tr><tr><td>[64]</td><td>SLIVER07, Spleen Dataset, Prostate</td><td>NAS</td><td>2020</td><td>MRI, CT</td><td>3D</td><td>Prostate, Spleen</td><td>N/A</td><td>N/A</td></tr></table>
<table><caption>Table 6: Comparison of multiple AutoML based medical image segmentation approaches with respect to different settings, GPU used and searching time. MSD in datasets used refers to datasets used from Medical Segmentation Decthalon (MSD). The method requiring less search time is highlighted with green color.</caption><tbody><tr><td>[45]</td><td>Real clinical dataset</td><td>NAS</td><td>2022</td><td>CT</td><td>2D</td><td>Prostate</td><td>N/A</td><td>N/A</td></tr><tr><td>[23]</td><td>Promise12, CHOAS</td><td>NAS</td><td>2022</td><td>MRI,CT</td><td>3D</td><td>Prostate, Spleen</td><td>N/A</td><td>N/A</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">Zero Cost Method</td></tr><tr><td>[139]</td><td>MSD liver, brain, BTCV</td><td>NAS</td><td>2023</td><td>CT/MRI</td><td>3D</td><td>Liver, Brain</td><td></td><td>20</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">Other Methods</td></tr><tr><td>[179]</td><td>MP-RAGE</td><td>NAS</td><td>2019</td><td>MRI T1</td><td>3D</td><td>Brain</td><td>3 Gpus Tesla v100</td><td>32.6</td></tr><tr><td>[129]</td><td>MSD</td><td>NAS</td><td>2022</td><td>MRI, CT, XRAY</td><td>3D</td><td>Spleen, Prostate, Pancrease, Lungs, Brain, Hip-pocampus</td><td>8 NVIDIA V100 GPUs</td><td>32</td></tr><tr><td>[189]</td><td>lung lesion/MSD/ Lits 2017</td><td>NAS+ HPO+ ADA</td><td>2021</td><td>CT</td><td>3D</td><td>Liver</td><td>2 NVIDIA V100 GPU</td><td>300</td></tr><tr><td>[172]</td><td>Choas, Promise12</td><td>NAS</td><td>2022</td><td>MRI,CT</td><td>3D</td><td>Prostate, Spleen</td><td>N/A</td><td>N/A</td></tr><tr><td>[155]</td><td>BRATS-2017</td><td>HPO</td><td>2020</td><td>MRI</td><td>3D</td><td>Brain</td><td>Nvidia Quadro K1200</td><td>N/A</td></tr></tbody></table>
<table><caption>Table 7: Comparison of reported DSC score by different studies on MSD and BraTS datasets. We have highlighted the best-reported results in green colors.</caption><thead><tr><th>Ref</th><th>Sliver</th><th>Spleen</th><th>Prostate</th><th>Hippo</th><th>Liver</th><th>Heart</th><th>CHOAS</th><th>PANCREASE</th><th>Promise</th><th>Brain</th><th>Lungs</th><th>ACDC (Avg)</th><th>Heptic Vessel</th><th>MoNuSeg</th><th>TNBC</th><th>Hepatic Vessels</th><th>Colon</th><th>ET</th><th>WT</th><th>TC</th><th>Searching time (In hours)</th></tr></thead><tbody><tr><td>[67]</td><td>0.831</td><td>0.848</td><td>0.88</td><td>0.846</td><td>0.948</td><td>0.885</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>60-110</td></tr><tr><td>[175]</td><td>0.891</td><td>0.837</td><td>0.838</td><td>0.826</td><td>0.924</td><td>0.845</td><td></td><td></td><td></td><td>0.653</td><td></td><td></td><td></td><td>0.802</td><td>0.696</td><td></td><td></td><td></td><td></td><td></td><td>39</td></tr><tr><td>[63]</td><td>0.940</td><td>0.941</td><td>0.874</td><td>0.893</td><td>0.959</td><td>0.887</td><td>0.889</td><td>0.734</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>288-912</td></tr><tr><td>[64]</td><td>0.943</td><td>0.95</td><td>0.92</td><td></td><td></td><td></td><td>0.921</td><td></td><td>0.902</td><td>0.767</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[14]</td><td>0.893</td><td>0.9</td><td>0.846</td><td></td><td></td><td></td><td>0.843</td><td></td><td>0.842</td><td>0.767</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>60-110</td></tr><tr><td>[122]</td><td></td><td>0.945</td><td>0.742</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[24]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.906</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>326.4</td></tr><tr><td>[44]</td><td></td><td></td><td>0.726</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.897</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>24</td></tr><tr><td>[155]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.82</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[172]</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.921</td><td></td><td>0.867</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[189]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.753</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>32</td></tr><tr><td>[129]</td><td></td><td></td><td></td><td>0.893</td><td></td><td></td><td></td><td>0.685</td><td></td><td>0.628</td><td>0.767</td><td></td><td>0.682</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[139]</td><td></td><td></td><td></td><td></td><td>0.851</td><td></td><td></td><td></td><td></td><td>0.657</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>20</td></tr><tr><td>[23]</td><td></td><td>0.959</td><td>0.679</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[25]</td><td></td><td></td><td></td><td>0.890</td><td></td><td></td><td></td><td></td><td></td><td>0.910</td><td></td><td></td><td>0.89</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>75</td></tr><tr><td>[170]</td><td></td><td>0.66</td><td></td><td></td><td>0.826</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>139.2</td></tr><tr><td>[188]</td><td></td><td>0.625</td><td></td><td></td><td>0.898</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>48</td></tr><tr><td>[28]</td><td></td><td>0.959</td><td>0.688</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>90</td></tr><tr><td>[31]</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.962</td><td></td><td>0.876</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[183]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.899</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[168]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[103]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>126.3</td></tr><tr><td>[152]</td><td></td><td></td><td>0.906</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.861</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[119]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.882</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>240</td></tr><tr><td>[108]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.917</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>N/A</td></tr><tr><td>[74]</td><td></td><td>0.969</td><td>0.969</td><td>0.891</td><td>0.849</td><td>0.929</td><td>0.924</td><td>0.681</td><td>0.662</td><td>0.625</td><td>0.747</td><td>0.681</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>139.2</td></tr><tr><td>[79]</td><td></td><td>0.998</td><td>0.831</td><td>0.894</td><td>0.858</td><td>0.967</td><td>0.933</td><td>0.672</td><td></td><td></td><td>0.739</td><td>0.691</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[203]</td><td></td><td>0.962</td><td>0.818</td><td>0.886</td><td>0.839</td><td>0.924</td><td></td><td>0.675</td><td></td><td></td><td>0.704</td><td>0.676</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[89]</td><td></td><td></td><td></td><td></td><td>0.912</td><td></td><td></td><td></td><td></td><td>0.729</td><td>0.686</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>24</td></tr><tr><td>[83]</td><td></td><td></td><td></td><td>0.763</td><td></td><td></td><td>0.935</td><td></td><td></td><td>0.745</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>36</td></tr><tr><td>[77]</td><td></td><td>0.974</td><td>0.819</td><td>0.896</td><td>0.865</td><td>0.933</td><td></td><td>0.685</td><td></td><td>0.614</td><td>0.760</td><td>0.691</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>48</td></tr><tr><td>[220]</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.552</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>30</td></tr><tr><td>[204]</td><td></td><td></td><td></td><td></td><td></td><td>0.951</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>2</td></tr><tr><td>[190]</td><td></td><td>0.920</td><td></td><td></td><td></td><td></td><td>0.920</td><td></td><td>0.519</td><td></td><td>0.449</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>48</td></tr><tr><td>[13]</td><td></td><td></td><td>0.755</td><td></td><td></td><td></td><td>0.927</td><td></td><td></td><td></td><td>0.741</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>33.36</td></tr></tbody></table>
In [191], a Transformer NAS approach has been proposed for 3D medical image segmentation. Their proposed algorithm attempts to search the global structure of the architecture and local operations within GPU memory consumption constraints. They used a Gradient-based search strategy and used One-Shot NAS as an approach to reduce the computational time. The proposed approach efficiently finds the concise relationship between convolutions and transformer layers in a single network. Although the approach achieves satisfactory results, it lacks searching for memory-efficient architectures, which can be possible by adding an extra memory constraint.

### 4.2.4. Discussion

The comparison of AutoML-based medical image segmentation approaches is given in Table 6. We have categorized these approaches based on their search strategies and reported their search time (in hours). One notable aspect of the search time is that some studies have mentioned only a single search time and have not provided further information about whether this search time is for a single dataset or multiple datasets. These approaches use NAS for automatically searching architecture, ADA for learning augmentation policy, HPO for optimizing architecture hyperparameters. The approaches requiring less search time among the optimization strategies are highlighted in green color.

The adoption of search strategies by these AutoML approaches greatly impacts the performance and quality of searched individuals. Different optimization approaches have been used for the search strategy, i.e., gradient-based approaches, evolutionary reinforcement, and fusions of different approaches. The performance based-comparison of AutoML approaches for medical image segmentation is not straightforward because these studies used different datasets, making it difficult to compare the performance of these approaches as given in Table 7. We compared studies that used datasets from the MSD benchmark. Most studies used this benchmark, which is well-known, to evaluate the performance of different AutoML approaches. The best reported results are highlighted in green colors.

Numerous evolutionary and gradient-based approaches for medical image segmentation using NAS have been proposed. Among them, super-net-based NAS approaches are found to be effective, both in terms of performance and searching time. In one-shot NAS, an architecture is first trained. Then, sub-networks are sampled to find an efficient architecture. Other performance estimation strategies are also effective to search an architecture for medical image segmentation task. Zero-cost proxies are shown to be effective, but only one study utilizes ZC proxies to increase the network's depth and width and evaluate it. Instead of brute-force approaches, optimization approaches can be combined with ZC and One-shot approaches to build efficient and hardware-aware medical image segmentation approaches.

Recent trends have shown that hybird CNN and transformer based approaches have been widely proposed for

medical image segmentation. These approaches have benefits from extracting features efficiently from both local (using CNN) and global contextual information (using transformers). Combining the spatial hierarchies captured by CNN and the ability of Transformers to model long-term dependencies enhances the overall image analysis and interpretation. Moreover, the NAS helps to find the optimal combination of transformers, CNN components, and hyper-parameters to enhance performance.

It can be noticed that there is still some room of improvement for these methods. Indeed, there is an increasing need for hardware-aware NAS approach for medical image segmentation to search for efficient lightweight encoder-decoder architectures.

## 4.3. Automatic Data Augmentation Approaches

Numerous studies have proposed to find task-driven data augmentation policies for different medical imaging tasks. The first data augmentation approach for 3D medical image segmentation is proposed in [190]. They used reinforcement learning to find the optimal augmentation policy. The drawback of their approach is that it approximately took 24 to 48 hours on 32 to 50 GPUs running in parallel to search for the best augmentation policy. Similarly, in [29], a data augmentation approach is proposed, which explicitly models the non-affine spatial transformation and intensity transformations and additive intensity masks to model shape and intensity variations. Evolutionary and gradient-based ADA approaches proposed for medical imaging are discussed in the following sub-sections. The disadvantage of this approach is that the generated images are not always realistic, as noted in the article, which can be counter-intuitive and potentially problematic for training robust models.

### 4.3.1. Evolutionary Approaches

Multiple approaches have been proposed to search augmentation policies for medical image classification. A crossover-based technique is proposed for data augmentation in medical image classification. They proposed a non-linear technique using two-point crossover to the existing image pixels to create new images. They evaluated the approach on breast mammograms, brain tumors, and skin cancer datasets. Results on multiple datasets demonstrate the effectiveness of the proposed approach in handling class imbalance and limited dataset problems [143].

Similarly, a modification on the RandAugment framework, a reinforcement learning framework is proposed to design an automated data augmentation policy for H&E stained histopathology slides. It improves the generalization ability of the performance of medical systems to classify histopathology images [50]. Moreover, they added domain-specific modifications relevant to histopathology images. Upon this modification, they achieved significant improvement on the CAMELYON-17 dataset. Although these evolutionary ADA techniques searches for a data augmentation policy by exploring the search space, their searching time is computationally expensive.
Lately, an evolutionary approach has been proposed to search for a data augmentation policy for the histopathological image classification task. They formulate the problem to represent different augmentation strategies [132]. ADA and NAS are jointly searched using evolutionary algorithms for medical image classification. They have proposed an augmented density matching approach by augmenting the train data with uniformly sampled policies from a distribution. During the search process, they proposed a unified search framework for data augmentation and neural architecture. During the one-shot NAS search process, the augmentation policy and network topology are learned using an evolutionary algorithm [208].

The advantage of simultaneously searching for both data augmentation strategies and architectural topology lies in the unified search process. Therefore, the need for a separate search stage for data augmentation is eliminated. However, this approach also enlarges the search space, potentially leading to incomplete exploration of all search space.

### 4.3.2. Gradient-based Approaches

Several gradient-based approaches have also been proposed. In [182], a differentiable approach is proposed for selecting a data augmentation strategy utilizing stochastic relaxation and the monte-carlo method for medical image segmentation. They adopted AutoAugment and used pre-defined augmentation transformations such as contrast enhancement or rotation to search optimal magnitude. Besides, a proximal updated based-data augmentation technique is proposed in [69]. A differentiable NAS method based on Proximal gradient descent which reforms the search process as an optimization problem is proposed. It can only use one operation to be updated in backward and forward propagation. They performed experiments on lungs dataset to segment pneumothorax.

Moreover, ADA and network topology are jointly searched using gradient-based approaches for medical image classification. He. et al. proposed a joint search framework to find optimal data augmentation policy and neural architectures. They used One-Shot NAS approach and data augmentation policy is learned alongside the network topology of sub-networks [70].

The benefit of jointly searching for an optimized data augmentation and architectural topology using gradient-based approach is the possibility of learning using a unified search process. This combination reduces the extra effort required to search for data augmentation as an extra stage. However, as the search space can be bigger, the optimization process can be limited when exploring it.

### 4.3.3. Other approaches

Numerous other techniques were introduced for automatic data augmentation. Gao et al. proposed to use adversarial training for ADA. They designed three augmentation models to learn different types of augmentations (affine, deformation, and appearance-based) [55]. Domain shift problems in histopathology is also handled by automatically

searching an architecture. In [50], to handle domain shift problem in histopathology images, an ADA approach for histopathology images is proposed. They extended the RandaAugment approach to build an efficient and robust data augmentation approach. The main advantage of their proposition is the limited number of the required hyperparameters (only number of transformations and magnitude) which significantly reduces the search space leading to faster optimization process. However, one limitation can be noticed, where the pre-defined set of transformations may not achieve a stable performance for all tasks.

Reinforcement learning techniques are also used for searching augmentation policy. Qin et. al [140] proposed a novel data augmentation strategy based on reinforcement learning for CT kidney tumor segmentation. They used a policy gradient-based approach for selecting the best augmentation policy. As an advantage, RL searches for an augment policy and tries to improve it over the iterations despite the high risk of overfitting due to its poor exploration capabilities.

### 4.3.4. Discussion

ADA strategies are helpful to improve the performance of medical image classification and segmentation approaches. Various optimization techniques have been employed for searching an optimal data augmentation policy for given problem. Evolutionary approaches exhibit a large search time unlike reinforcement learning and gradient-based techniques. As evolutionary techniques tries multiple combinations but gradient-based approaches explore a single solution. Hybrid NAS-ADA approaches were proven to be effective as the search space consists of both architectural topology and data augmentation techniques. Recent combined NAS+ADA evolutionary and gradient based approaches have achieved state-of-the art results on MedMNIST benchmark datasets. Using combined NAS+ADA approach for medical image segmentation and other medical image applications would be a promising future research direction.

## 4.4. AutoML based Medical Image Denoising

While obtaining medical image scans from medical devices, images may be corrupted by artifacts or noise during the process of acquisition or processing stages. A random change in the pixel values can describe the noise. Noise reduces the image quality and is especially significant when the captured objects are of low contrast. Early approaches use traditional image processing filters to remove noise, but thanks to advancements in deep learning algorithms, various CNN-based architectures have been proposed in the literature to address the issue [87].

### 4.4.1. Evolutionary approaches

Numerous studies have been proposed to design CNN architectures for medical image denoising. Some studies use GA to evolve CNN architectures, such as in [101], where the authors proposed a GA-based approach to evolve CNN architecture for medical image denoising problems. An
<figure><img src="image_20.png" /><figcaption>Figure 15: Medical Segmentation decathlon consisting of multiple organs datasets. Image taken from [11]</figcaption></figure>

experience-based greedy exploration approach with transfer
learning is adopted to find optimal architecture having better
performance. Similarly, in [102], The authors proposed an-
other GA-based approach incorporated with an experience-
based greedy exploration approach, alongside a transfer
learning mechanism to find optimal architecture having bet-
ter performance.

Moreover, a DE-based approach for designing block-
based networks is proposed for medical image denoising. An
efficient algorithm to find the best-performing architecture
was developed. Their approach, DEvoNet, finds network
structure and parameters consisting of residual and dense
blocks. The proposed approach demonstrates effectiveness
on multiple modalities that simplify CNN parameter se-
lection, such as activation functions, network blocks, and
optimizers as vector parameters in the evolutionary process
[144].

To the best of our knowledge performance estimation
strategies such as surrogate models haven't been Incorpo-
rated for this task which would be beneficial in reducing the
searching time.

**4.4.2. Gradient-based approaches**

Gradient-based approaches have also been proposed for
medical image denoising. In[109], the authors proposed an
approach for low-dose denoising of CT images. They used
a multi-scale and multi-level search space. The multi-scale
and multi-level feature extraction enhances the denoising
process. Despite the effectiveness of their proposition, the
increasing complexity exhibited by multi-scale and multi-
level cells remains a serious issue.

**4.4.3. Discussion**

Although only a limited number of studies have been
proposed for automatic medical image denoising, these stud-
ies have utilized various approaches, including evolution-
ary and gradient-based approaches. It is worth noting that

gradient-based approaches are efficient as they have used
the One-Shot NAS approach, which uses a weight-sharing
mechanism to reduce the search time.

**4.5. Medical Imaging Benchmarking Datasets for AutoML Approaches**

For benchmarking and comparison of multiple NAS-
based approaches on medical image segmentation and clas-
sification tasks, two datasets, namely MedMNIST (Medical
MNIST) and Medical Segmentation Decathlon challenge,
have been mostly used by different studies. In MedMNIST,
a benchmark for medical image classification is proposed
gathered from multiple public datasets of different organs
and modalities [192]. The visualization of different datasets
in MedMNIST is shown in Figure 16 and MSD is shown
in Figure 15. These images are pre-processed by resizing
and normalizing the images to have the same size. They
evaluated the proposed benchmark on multiple AutoML
libraries and the Google AutoML platform. Moreover, for
evaluating NAS for medical image segmentation, Medical
Segmentation Decathlon (MSD) is used, which is a dataset
belonging to the MSD analysis challenge that consists of
ten different datasets of different organs and modalities to
evaluate the model compatibility to perform well on mul-
tiple tasks and generalize well to previously unseen tasks
[161]. Although this challenge is designed for segmentation
algorithms, this dataset was used by most of the NAS-based
approaches for medical image segmentation to guarantee a
fair comparison with existing NAS approaches.

**5. Analysis and Discussion**

In our study on applications of AutoML in medical
image analysis, we have categorized the reviewed studies
into distinct themes, encompassing a wide range of med-
ical imaging modalities and tasks. We found an increased
trend of adaptation of AutoML for numerous medical image
<figure><img src="image_21.png" /><figcaption>**Figure 16:** MedMNIST datasets consisting of multiple organs and modalities. Image taken from [192].</figcaption></figure>

analysis tasks achieving state-of-the-art performance. The major idea is to build fast, efficient, and hardware-efficient AutoML approaches, which is addressed by the proposed studies. Indeed, several tasks were tackled using a variety of optimization strategies along with performance estimation strategies.

These optimization approaches directly impact the performance when performing medical imaging tasks. They include gradient-based, evolutionary, reinforcement learning, and hybrid approaches. As evaluating each individual (network architecture) is time-consuming, several performance estimation strategies such as one-shot, surrogate-based, and zero-cost proxies have been used to decrease the search time. The literature analysis shows that evolutionary and gradient-based super-net approaches have achieved state-of-the-art performance in medical image classification and segmentation tasks. Super-net-based evolutionary and gradient-based NAS algorithms have been widely adopted in different medical imaging tasks as they use the weight-sharing mechanism in which the retraining of the model is no longer necessary. Data augmentation is also searched along with network topology in super-net-based evolutionary and gradient-based approaches. Data augmentation can handle overfitting and limited data issues jointly learned with the network topology. Recently, zero-cost proxies have been adopted for medical image segmentation and classification tasks with a decreasing searching time compared to existing approaches.

Evolutionary approaches have also shown to be effective while dealing with multiple conflicting objectives. Evolutionary approaches use nature's selection process to select

the fittest individuals using mutation and recombination strategies. These approaches have been used to search for hardware-aware NAS for medical imaging tasks, in which multiple conflicting objectives with trade-offs such as accuracy, number of parameters and latency are present.

Although several AutoML-based approaches have been proposed for different medical image analysis tasks, these methods still have a considerable gap for improvement. Many issues, such as uncertainty segmentation, still need to be explored. For further analysis, we have categorized the search strategies used to solve different AutoML problems as shown in Table 10. It demonstrates that various search strategies have been proposed for numerous tasks. Furthermore, the list of medical imaging studies proposed for different AutoML tasks is shown in Table 9 and the list of medical imaging tasks is shown in Table 8. It covers the list of the studies proposed related to the mentioned categories. It can be revealed that the most trending medical imaging task adopted to solve AuotML is segmentation using NAS and HPO. As segmentation is one of the most important tasks, designing and tuning parameters is challenging.

# 6. Current Trends and Open Challenges

An important number of approaches for solving medical imaging tasks using AutoML approaches is proposed. However, serious challenges have to be addressed in this domain unveiling interesting potential research directions for future research. These challenges and directions include:
<table><caption>**Table 8**
Number of articles published in different categories related to tasks in Medical Image Analysis</caption><tr><td>Segmentation</td><td>[23],[172],[31],[108],[63],[218],[44],[217],[183],[77],[213],[29],[55],[174],[42],[152],[122],[182],[189],[43],[56],[94],[185],[24],[103],[204][134] [146] [104] [201] [151] [200] [88] [91] [115][218][76] [217][135] [54] [139] [45] [44] [14], [25], [119], [220], [89], [26], [13],[66],[179],[203],[61],[168] [49][190],[188],[57],[83],[74],[170]</td></tr><tr><td>Denoising</td><td>[101],[102][109] [110]</td></tr><tr><td>Classification</td><td>[39],[38],[113], [15],[157],[92],[93],[85],[125],[92],[62],[70],[36],[218],[163],[55],[112],[124],[123],[7][33][37][117] [197][180][187][162][18][169] [193] [58] [196] [171] [12],[96],[142],[156],[48],[192],[211],[17],[22],[210],[98],[90],[167],[82],[207],[192], [51],[131],[71],[72],[141],[165],[52]</td></tr><tr><td>Regression</td><td>[133],[5]</td></tr></table>

<table><caption>**Table 9**
Number of articles published in different categories related to AutoML in Medical Image Analysis</caption><tr><td>Neural Architecture Search</td><td>[218][217][44][36][38][93][31][108][85][125][92][63][62][163][77] [183][172][23] [149][212][39][95][137] [92][112][67][64][64][129] [155][59][105][102][124][174][131][188][57][194] [83][107][71][72][74][170][185][94][56][43][175][141][165][189][25][44][113][15][30][139][135][169][45][134][115][18][162][187][199][117][76][180][109][110][148][204][197][103][37][13][66][179][101][203][49][186][61][205][168][136] [57][194]</td></tr><tr><td>Hyperparameter Optimization</td><td>[14][98][210][122][75][22][211][142][133][12][123]</td></tr><tr><td>Automatic Data Augmentation</td><td>[190] [182] [29] [55] [213] [70]</td></tr><tr><td>AutoML Benchmark</td><td>[192]</td></tr><tr><td>AutoML Tools used</td><td>[207] [82] [167] [156] [90]</td></tr><tr><td>Model Selection</td><td>[17]</td></tr><tr><td>Combined Hyperparameter Optimization and Neural Architecture Search</td><td>[24] [96]</td></tr><tr><td>Automatic Explainable AI</td><td>[157]</td></tr><tr><td>Network Architecture Adoption</td><td>[84]</td></tr></table>

* **Searching Time:** The computational time of AutoML approaches for medical image applications is expensive. To overcome this issue, surrogate-based and one-shot NAS approaches have been proposed. Exploring zero-shot proxies and one-shot NAS in different medical imaging tasks is a possible research direction.

* **Explainability:** Building NAS, ADA, and HPO approaches evaluating architectures based on their interpretability/explainability for medical image applications opens new research directions, as the latter is essential to build applications that can explain how they make decisions.

* **Reproducability:** Most of the AutoML approaches proposed for medical image applications are not publicly shared. In terms of reproducability, it is difficult for future researchers to explore new research directions and compare with their approaches.

* **Toolkit:** Although some platforms can perform the classification of medical images, no such platform is built explicitly for different medical image analysis tasks (Prognosis, Registration, Denoising, Reconstruction) using AutoML techniques. There is a need for a toolkit that can perform multiple tasks utilizing AutoML pipelines designed to handle medical data.

* **Other areas:** To the best of our knowledge, most of the proposed approaches are focused on Classification and Segmentation. Indeed, a small number of studies is addressing medical image reconstruction or denoising.

* **Benchmarking:** NAS benchmark is a dataset consisting of network representations from a specific search space. It is designed with a set of evaluation metrics and other log information. Numerous NAS benchmarks were introduced for different tasks such as NLP, HW-NAS, GNN, image classification, etc [34].
Unfortunately, there is no NAS benchmark built for
medical image classification and segmentation tasks.
Therefore, designing a new benchmark can ulitmately
help evaluating the performance of novel proposals.

*   **Joint HPO+NAS+DAS:** Jointly optimizing the Data Augmentation Strategy (DAS), hyperparameters, architecture, Loss function, and optimizer for medical imaging tasks is also one of the possible research directions.

*   **Learning with limited data with NAS for medical images:** A limited number of studies have used NAS to search for an architecture capable of learning with limited data, such as self-supervised, weekly supervised, and semi-supervised-related tasks. Employing these techniques with NAS for medical images is also a potential research direction.

*   **Medical Data Compression:** NAS can be used to design neural networks for medical data compression, such as compressing medical images and videos for storage or transmission.

*   **Combining LLM with NAS for medical images applications:** Large Language Models (LLMs) have been recently adopted to improve the network representation or search for an architectural topology in NAS [202] [202]. Adopting LLM can be an alternative choice for medical image applications.

*   **Hardware-Aware NAS for medical images:** Designing Hardware Aware NAS algorithms which can perform medical image analysis tasks with respect to hardware and performance handling multiple objectives.

*   **Transformer NAS for medical images:** There are only limited studies that search transformer architectures for medical image classification. However, transformers are effective in different medical imaging tasks. Indeed, there is a gap for improvement utilizing transformer approaches.

7. Conclusion

AutoML approaches have achieved state-of-the-art per-
formance and have been effectively applied to solve a wide
range of medical image applications. In this study, recently
proposed AutoML algorithms for solving medical image
analysis tasks were thoroughly discussed and categorized.
We provided an overview of recent advances in this emerg-
ing field, summarized recent state-of-the-art approaches, and
discussed the effectiveness of these approaches compared to
traditional ones. The analysis of the state-of-the-art literature
shows that most of the studies focus on solving medical
image segmentation and classification tasks. regarding these
approaches, the search time and hardware efficiency are the
most important factors in this research area. Furthermore,

One Shot NAS-based approach is effective, and zero-cost
proxies have recently been adopted showing promising re-
sults. They can be explored in other research areas such as
medical image reconstruction and/or denoising. Moreover,
future research directions are proposed to introduce novel
studies in this field.

8. Acknowledgement

**Funding:** This work was supported by the ArtIC project "Artificial Intelligence for Care" [grant number ANR-20-THIA-0006-01]; and co-funded by IRIMAS Institute / Université de Haute Alsace. The authors would like to thank the reviewers for their kind suggestions to improve the quality of the manuscript.

CRediT authorship contribution statement

**Muhammad Junaid Ali:** Conceptualization, Writing
Original Draft, Formal analysis, Methodology, Visualiza-
tion. **Mokhtar Essaid:** Conceptualization, Review & Edit-
ing, Supervision, Project administration. **Laurent Moalic:**
Conceptualization, Review & Editing, Supervision, Project
administration. **Lhassane Idoumghar:** Conceptualization,
Review & Editing, Supervision, Project administration.

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