ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep learning for lungs cancer detection: a review
Authors: Rabia Javed, Tahir Abbas, Ali Haider Khan, Ali Daud, Amal Bukhari, Riad Alharbey

Although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. Medical practitioners’ burden is reduced by deep learning techniques, especially Deep Convolutional Neural Networks (DCNN), which are essential in automating the diagnosis and classification of diseases. In this study, we use a variety of medical imaging modalities, including X-rays, WSI, CT scans, and MRI, to thoroughly investigate the use of deep learning techniques in the field of lung cancer diagnosis and classification. This study conducts a comprehensive Systematic Literature Review (SLR) using deep learning techniques for lung cancer research, providing a comprehensive overview of the methodology, cutting-edge developments, quality assessments, and customized deep learning approaches. It presents data from reputable journals and concentrates on the years 2015–2024. Deep learning techniques solve the difficulty of manually identifying and selecting abstract features from lung cancer images. This study includes a wide range of deep learning methods for classifying lung cancer but focuses especially on the most popular method, the Convolutional Neural Network (CNN). CNN can achieve maximum accuracy because of its multi-layer structure, automatic learning of weights, and capacity to communicate local weights. Various algorithms are shown with performance measures like precision, accuracy, specificity, sensitivity, and AUC; CNN consistently shows the greatest accuracy. The findings highlight the important contributions of DCNN in improving lung cancer detection and classification, making them an invaluable resource for researchers looking to gain a greater knowledge of deep learning’s function in medical applications.
A brief review of hypernetworks in deep learning
Authors: Vinod Kumar Chauhan, Jiandong Zhou, Ping Lu, Soheila Molaei, David A. Clifton

Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. Despite their success across different problem settings, there is currently no comprehensive review available to inform researchers about the latest developments and to assist in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example of training deep neural networks using hypernets and propose categorizing hypernets based on five design criteria: inputs, outputs, variability of inputs and outputs, and the architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain underexplored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks.
Learning Dynamic Batch-Graph Representation for Deep Representation Learning
Authors: Xixi Wang, Bo Jiang, Xiao Wang, Bin Luo

Recently, batch-based image data representation has been demonstrated to be effective for context-enhanced image representation. The core issue for this task is capturing the dependences of image samples within each mini-batch and conducting message communication among different samples. Existing approaches mainly adopt self-attention or local self-attention models (on patch dimension) for this task which fail to fully exploit the intrinsic relationships of samples within mini-batch and also be sensitive to noises and outliers. To address this issue, in this paper, we propose a flexible Dynamic Batch-Graph Representation (DyBGR) model, to automatically explore the intrinsic relationship of samples for contextual sample representation. Specifically, DyBGR first represents the mini-batch with a graph (termed batch-graph) in which nodes represent image samples and edges encode the dependences of images. This graph is dynamically learned with the constraint of similarity, sparseness and semantic correlation. Upon this, DyBGR exchanges the sample (node) information on the batch-graph to update each node representation. Note that, both batch-graph learning and information propagation are jointly optimized to boost their respective performance. Furthermore, in practical, DyBGR model can be implemented via a simple plug-and-play block (named DyBGR block) which thus can be potentially integrated into any mini-batch based deep representation learning schemes. Extensive experiments on deep metric learning tasks demonstrate the effectiveness of DyBGR. We will release the code at https://github.com/SissiW/DyBGR.
Relay learning: a physically secure framework for clinical multi-site deep learning
Authors: Zi-Hao Bo, Yuchen Guo, Jinhao Lyu, Hengrui Liang, Jianxing He, Shijie Deng, Feng Xu, Xin Lou, Qionghai Dai

Big data serves as the cornerstone for constructing real-world deep learning systems across various domains. In medicine and healthcare, a single clinical site lacks sufficient data, thus necessitating the involvement of multiple sites. Unfortunately, concerns regarding data security and privacy hinder the sharing and reuse of data across sites. Existing approaches to multi-site clinical learning heavily depend on the security of the network firewall and system implementation. To address this issue, we propose Relay Learning, a secure deep-learning framework that physically isolates clinical data from external intruders while still leveraging the benefits of multi-site big data. We demonstrate the efficacy of Relay Learning in three medical tasks of different diseases and anatomical structures, including structure segmentation of retina fundus, mediastinum tumors diagnosis, and brain midline localization. We evaluate Relay Learning by comparing its performance to alternative solutions through multi-site validation and external validation. Incorporating a total of 41,038 medical images from 21 medical hosts, including 7 external hosts, with non-uniform distributions, we observe significant performance improvements with Relay Learning across all three tasks. Specifically, it achieves an average performance increase of 44.4%, 24.2%, and 36.7% for retinal fundus segmentation, mediastinum tumor diagnosis, and brain midline localization, respectively. Remarkably, Relay Learning even outperforms central learning on external test sets. In the meanwhile, Relay Learning keeps data sovereignty locally without cross-site network connections. We anticipate that Relay Learning will revolutionize clinical multi-site collaboration and reshape the landscape of healthcare in the future.
Predicting Apple Plant Diseases in Orchards Using Machine Learning and Deep Learning Algorithms
Authors: Imtiaz Ahmed, Pramod Kumar Yadav

Apple cultivation in the Kashmir Valley is a cornerstone of the region’s agriculture, contributing significantly to the economy through substantial annual apple exports. This study explores the application of machine learning and deep learning algorithms for predicting apple plant diseases in orchards. By leveraging advanced computational techniques, the research aims to enhance early detection and diagnosis of diseases, thereby enabling proactive disease management. The study utilizes a dataset comprising diverse environmental and plant health factors to train and validate the models. Key highlights include the comparative analysis of machine learning and deep learning approaches, the identification of optimal feature sets, and the assessment of model performance. The findings contribute to the development of efficient and accurate tools for precision agriculture, facilitating timely intervention and sustainable orchard management. The apple industry in Kashmir faces a significant challenge due to the prevalence of various diseases affecting apple trees. One prominent disease that adversely impacts apple yields in the region is the Apple Scab, caused by the fungus Venturia inadequacies. Apple Scab is characterized by dark, scaly lesions on leaves, fruit, and twigs, leading to defoliation and reduced fruit quality. The disease thrives in cool and humid conditions, which are prevalent in the Kashmir Valley. This study addresses the limitations of traditional, labor-intensive, and time-consuming laboratory methods for diagnosing apple plant diseases. The goal is to provide an accurate and efficient deep learning-based system for the prompt identification and prediction of foliar diseases in Kashmiri apple plants. Our study begins involves the creation of a dataset annotated by experts containing approximately 10,000 high-quality RGB images that illustrate key symptoms associated with foliar diseases. In the next step, an approach to deep learning that utilizes convolutional neural networks (CNNs) was developed. Comparative analysis five different deep learning algorithms, including Faster R-CNN, showed that the method was effective in detecting apple diseases in real time. The proposed framework, when tested, achieves state-of-the-art results with a remarkable 92% accuracy in identifying apple plant diseases. A new dataset is presented that includes samples of leaves from Kashmiri apple plants that have three different illnesses. The findings hold promise for revolutionizing orchard management practices, ultimately benefiting apple growers and sustaining the thriving apple industry in the Kashmir Valley.
Deep Learning zur  Kariesdiagnostik
Authors: Norbert Krämer, Roland Frankenberger

Deep-Learning-Modelle spielen auch in der Zahnheilkunde eine zunehmend größere Rolle und werden in unterschiedlichen Feldern eingesetzt. Vor diesem Hintergrund wurde in der vorliegenden Literaturübersicht eine systematische Übersichtsarbeit einer internationalen Autorengruppe vorgestellt, die Deep-Learning-Modelle zur Kariesdiagnostik analysierte und bewertete. Sie kam zu dem Schluss, dass in einer zunehmenden Anzahl von Studien die Kariesdiagnostik mithilfe von Deep-Learning-Modellen unterstützt wird. Die dokumentierte Genauigkeit erscheint vielversprechend, während Studien- und Berichtsqualität derzeit unzureichend sind, um weiterführende Analysen durchzuführen. Bei verbesserter Datenlage könnten jedoch künftig Deep-Learning-Modelle als Hilfsmittel für Entscheidungen über das Vorhandensein von kariösen Läsionen herangezogen werden.
Deep doubly robust outcome weighted learning
Authors: Xiaotong Jiang, Xin Zhou, Michael R. Kosorok

Precision medicine is a framework that adapts treatment strategies to a patient’s individual characteristics and provides helpful clinical decision support. Existing research has been extended to various situations but high-dimensional data have not yet been fully incorporated into the paradigm. We propose a new precision medicine approach called deep doubly robust outcome weighted learning (DDROWL) that can handle big and complex data. This is a machine learning tool that directly estimates the optimal decision rule and achieves the best of three worlds: deep learning, double robustness, and residual weighted learning. Two architectures have been implemented in the proposed method, a fully-connected feedforward neural network and the Deep Kernel Learning model, a Gaussian process with deep learning-filtered inputs. We compare and discuss the performance and limitation of different methods through a range of simulations. Using longitudinal and brain imaging data from patients with Alzheimer’s disease, we demonstrate the application of the proposed method in real-world clinical practice. With the implementation of deep learning, the proposed method can expand the influence of precision medicine to high-dimensional abundant data with greater flexibility and computational power.
Topological deep learning: a review of an emerging paradigm
Authors: Ali Zia, Abdelwahed Khamis, James Nichols, Usman Bashir Tayab, Zeeshan Hayder, Vivien Rolland, Eric Stone, Lars Petersson

Topological deep learning (TDL) is an emerging area that combines the principles of Topological data analysis (TDA) with deep learning techniques. TDA provides insight into data shape; it obtains global descriptions of multi-dimensional data whilst exhibiting robustness to deformation and noise. Such properties are desirable in deep learning pipelines, but they are typically obtained using non-TDA strategies. This is partly caused by the difficulty of combining TDA constructs (e.g. barcode and persistence diagrams) with current deep learning algorithms. Fortunately, we are now witnessing a growth of deep learning applications embracing topologically-guided components. In this survey, we review the nascent field of topological deep learning by first revisiting the core concepts of TDA. We then explore how the use of TDA techniques has evolved over time to support deep learning frameworks, and how they can be integrated into different aspects of deep learning. Furthermore, we touch on TDA usage for analyzing existing deep models; deep topological analytics. Finally, we discuss the challenges and future prospects of topological deep learning.
Deep learning in computational mechanics: a review
Authors: Leon Herrmann, Stefan Kollmannsberger

The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies within this field, we provide an overview of deep learning in deterministic computational mechanics. Five main categories are identified and explored: simulation substitution, simulation enhancement, discretizations as neural networks, generative approaches, and deep reinforcement learning. This review focuses on deep learning methods rather than applications for computational mechanics, thereby enabling researchers to explore this field more effectively. As such, the review is not necessarily aimed at researchers with extensive knowledge of deep learning—instead, the primary audience is researchers on the verge of entering this field or those attempting to gain an overview of deep learning in computational mechanics. The discussed concepts are, therefore, explained as simple as possible.
OpBench: an operator-level GPU benchmark for deep learning
Authors: Qingwen Gu, Bo Fan, Zhengning Liu, Kaicheng Cao, Songhai Zhang, Shimin Hu

Operators (such as Conv and ReLU) play an important role in deep neural networks. Every neural network is composed of a series of differentiable operators. However, existing AI benchmarks mainly focus on accessing model training and inference performance of deep learning systems on specific models. To help GPU hardware find computing bottlenecks and intuitively evaluate GPU performance on specific deep learning tasks, this paper focuses on evaluating GPU performance at the operator level. We statistically analyze the information of operators on 12 representative deep learning models from six prominent AI tasks and provide an operator dataset to show the different importance of various types of operators in different networks. An operator-level benchmark, OpBench, is proposed on the basis of this dataset, allowing users to choose from a given range of models and set the input sizes according to their demands. This benchmark offers a detailed operator-level performance report for AI and hardware developers. We also evaluate four GPU models on OpBench and find that their performances differ on various types of operators and are not fully consistent with the performance metric FLOPS (floating point operations per second).
Diabetes detection based on machine learning and deep learning approaches
Authors: Boon Feng Wee, Saaveethya Sivakumar, King Hann Lim, W. K. Wong, Filbert H. Juwono

The increasing number of diabetes individuals in the globe has alarmed the medical sector to seek alternatives to improve their medical technologies. Machine learning and deep learning approaches are active research in developing intelligent and efficient diabetes detection systems. This study profoundly investigates and discusses the impacts of the latest machine learning and deep learning approaches in diabetes identification/classifications. It is observed that diabetes data are limited in availability. Available databases comprise lab-based and invasive test measurements. Investigating anthropometric measurements and non-invasive tests must be performed to create a cost-effective yet high-performance solution. Several findings showed the possibility of reconstructing the detection models based on anthropometric measurements and non-invasive medical indicators. This study investigated the consequences of oversampling techniques and data dimensionality reduction through feature selection approaches. The future direction is highlighted in the research of feature selection approaches to improve the accuracy and reliability of diabetes identifications.
Deep-kidney: an effective deep learning framework for chronic kidney disease prediction
Authors: Dina Saif, Amany M. Sarhan, Nada M. Elshennawy

Chronic kidney disease (CKD) is one of today’s most serious illnesses. Because this disease usually does not manifest itself until the kidney is severely damaged, early detection saves many people’s lives. Therefore, the contribution of the current paper is proposing three predictive models to predict CKD possible occurrence within 6 or 12 months before disease existence namely; convolutional neural network (CNN), long short-term memory (LSTM) model, and deep ensemble model. The deep ensemble model fuses three base deep learning classifiers (CNN, LSTM, and LSTM-BLSTM) using majority voting technique. To evaluate the performance of the proposed models, several experiments were conducted on two different public datasets. Among the predictive models and the reached results, the deep ensemble model is superior to all the other models, with an accuracy of 0.993 and 0.992 for the 6-month data and 12-month data predictions, respectively.
Predicting Potato Crop Yield with Machine Learning and Deep Learning for Sustainable Agriculture
Authors: El-Sayed M. El-Kenawy, Amel Ali Alhussan, Nima Khodadadi, Seyedali Mirjalili, Marwa M. Eid

Potatoes are an important crop in the world; they are the main source of food for a large number of people globally and also provide an income for many people. The true forecasting of potato yields is a determining factor for the rational use and maximization of agricultural practices, responsible management of the resources, and wider regions’ food security. The latest discoveries in machine learning and deep learning provide new directions to yield prediction models more accurately and sparingly. From the study, we evaluated different types of predictive models, including K-nearest neighbors (KNN), gradient boosting, XGBoost, and multilayer perceptron that use machine learning, as well as graph neural networks (GNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTM), which are popular in deep learning models. These models are evaluated on the basis of some performance measures like mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) to know how much they accurately predict the potato yields. The terminal results show that although gradient boosting and XGBoost algorithms are good at potato yield prediction, GNNs and LSTMs not only have the advantage of high accuracy but also capture the complex spatial and temporal patterns in the data. Gradient boosting resulted in an MSE of 0.03438 and an R2 of 0.49168, while XGBoost had an MSE of 0.03583 and an R2 of 0.35106. Out of all deep learning models, GNNs displayed an MSE of 0.02363 and an R2 of 0.51719, excelling in the overall performance. LSTMs and GRUs were reported to be very promising as well, with LSTMs comprehending an MSE of 0.03177 and GRUs grabbing an MSE of 0.03150. These findings underscore the potential of advanced predictive models to support sustainable agricultural practices and informed decision-making in the context of potato farming.
Prediction of glycopeptide fragment mass spectra by deep learning
Authors: Yi Yang, Qun Fang

Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.
Deep learning application in diagnosing breast cancer recurrence
Authors: Zeinab Jam, Amir Albadvi, Alireza Atashi

Patients' lives can always be saved when diseases, especially special diseases, are detected early. The chances of a patient surviving can be increased by early detection. Breast cancer is one of the deadliest and common cancers. After recovering from breast cancer, patients are always worried about recurrence and return. The use of modern technology, however, can help predict disease recurrence at an early stage, allowing patients to receive treatment sooner.Significant strides have been achieved in deep learning, demonstrating strong performance in handling unstructured data challenges. However, when it comes to predicting tabular data, deep learning hasn't quite matched its success with unstructured data. Presently, ensemble models relying on gradient-boosted decision trees (GBDT) are frequently favored for tabular data prediction tasks. Typically, these GBDT-based models outshine deep learning approaches.Many novel deep learning techniques are emerging for handling tabular data. TabNet, for instance, mirrors decision tree feature selection within a neural network framework. AutoInt addresses high dimensionality by condensing data through embedding layers. Tab Transformer adapts the transformer model, generating text representations for categorical attributes. Despite their innovation, these methods remain less recognized compared to those for image and text data processing.In this study, 158 different characteristics of 5142 breast cancer patients from 1997 to 2019 were examined. We aim to evaluate deep learning techniques effectiveness in detecting breast cancer recurrence. Through examination of evaluation metrics, it becomes evident that deep learning approaches applied to tabular data surpass traditional machine learning algorithms, even when dealing with imbalanced datasets. Ultimately, the results derived from each algorithm analyzed and concluded with a review and comparison of the findings.