Reviewerly/exHarmony huggingface dataset:
SCTransNet: 3D Medical Image Segmentation Model Based on the Fusion of CNN and Transformer

Deep learning has played an important role in medical image segmentation of liver and liver tumors, but existing models are still insufficient in accuracy and efficiency. In this paper, We proposed SCTransNet, a 3D image segmentation model based on the fusion of CNN and Transformer model. SCTransNet combines the feature extraction and expression capabilities of convolutional neural network(CNN) and the long-distance dependency modeling capability of Transformer model. SCTransNet improves the embedding layer and position encoding layer of the Transformer model to enhance the global contextual feature extraction ability. Meanwhile, a spatial attention module and a channel attention module based on improved Transformer model are designed in SCTransNet to enhance the feature extraction ability of low-dimensional pixel information and high-dimensional semantic information. Experimental results on the public dataset show that SCTransNet achieve relatively better performance than state-of-the-art methods.
Semi-supervised Medical Image Segmentation via Feature-perturbed Consistency

Although deep convolutional neural networks have achieved satisfactory performance in many medical image segmentation tasks, a considerable annotation challenge still needs to be solved, which is expensive and time-consuming for radiologists. Most existing popular semi-supervised methods mainly impose data-level perturbations (e.g., rotation, noising) or feature-level perturbations (e.g., MC dropout) on unlabeled data. In this paper, we propose a novel semi-supervised segmentation strategy with meaningful perturbations at the feature level to leverage abundant useful information naturally embedded in the unlabeled data. Specifically, we develop a dual-task network where the segmentation head produces multiple predictions with a perturbation module, and the reconstruction head further utilizes the semantic information to enhance segmentation performance. The proposed framework subtly perturbs the network at the feature-level to generate predictions which should be similar and consistent. However, enforcing them roughly to be consistent at all pixels harms stable training and neglects much delicate information. To better utilize those predictions and estimate the uncertainty, we further propose feature-perturbed consistency to exploit reliable regions for our framework to learn from. Extensive experiments on the public BraTS2020 dataset and the 2017 ACDC dataset confirm the efficiency and effectiveness of our method. In particular, the proposed method demonstrates remarkable superiority in the segmentation of boundary regions. The project is available at https://github.com/youngyzzZ/SFPC.
Fast MRI Reconstruction via Boosting Filter Diversity of Deep Cascading Networks

Deep Cascading Networks (DCNs) are very popular for fast MRI reconstruction. However, DCNs still have limited generalization ability on highly undersampled MRI data. One main reason is that the training data is not well used. A promising solution is to boost the filter diversity of DCNs to well fit the rich features in the training data. This can be achieved by reducing the repetition level of the undersampled input images via using the pixel unshuffle (PU) operator. As different input images and different subnets of DCNs require different PU-scales, we propose a novel PU-Scale Estimation (PUSE) method to automatically infer optimal PU-scales. By incorporating PUSE into DCNs, we construct a new Multi-PU-Scale Diversity based (MSDiv+) architecture for DCNs. To boost training convergence, we further propose to generate mini-batches by mixing data samples with different optimal PU-scales. Experiments on the fastMRI dataset demonstrate the effectiveness of our method.
Keypoint-Driven Unsupervised Learning for Histopathology Image Registration

In this research, a new keypoint-centric training technique is presented, targeting efficient and precise unsupervised training of localized patch-based models applicable to histopathological whole slide image (WSI). The challenges imposed by the enormous gigapixel size of WSI and possible staining inconsistencies have historically hindered high-level training of local patch-based models. The method proposed here utilizes local patches focused around identified keypoints, thereby simplifying the training process for both local affine and non-rigid models. Separate networks were employed for training, and the method was assessed using local patch pairs from a dataset supplied by the University Hospital Frankfurt, consisting of 36 multi-stained histopathological WSIs. The subsequent experimental comparison shows that the keypoint-based approach enhances registration accuracy in both local affine and non-rigid contexts. These results affirm that the keypoint-oriented training method could serve as a significant step forward in improving the accuracy and efficiency of unsupervised learning within the intricate realm of histopathological image analysis, thus contributing to potential progress in image processing and medical diagnosis.
Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology

Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various image corruptions, making it difficult for deep neural network (DNN) models to achieve stable diagnostic results for clinical use. In order to assess and further enhance the robustness of the models, we analyze the physical causes of the full-stack corruptions throughout the pathological life-cycle and propose an Omni-Corruption Emulation (OmniCE) method to reproduce 21 types of corruptions quantified with 5-level severity. We then construct three OmniCE-corrupted benchmark datasets at both patch level and slide level and assess the robustness of popular DNNs in classification and segmentation tasks. Further, we explore to use the OmniCE-corrupted datasets as augmentation data for training and experiments to verify that the generalization ability of the models has been significantly enhanced.
Adversarial Training with Comprehensive Objective for Medical Image Report Generation

Medical Image Report Generation aims to automatically generate medical reports based on radiology images, thus freeing radiologists from the tedious task of writing reports. Generating report texts that match the content of a given medical image, focus on local anomalies, and fluently conform to professional reporting norms presents the challenge of linking visual patterns to informative human-language descriptions. In this paper, we propose a novel generative adversarial framework (MIRGAN) to guide the generator to generate medical reports that are indistinguishable from those written by professional radiologists. MIRGAN introduces a multimodal discriminator to evaluate the performance of the report generator on the comprehensive objective containing three sub-objectives when generating each token. MIRGAN then uses it as the reward in reinforcement learning to guide the generator to optimize toward the desired objective in real time. We conduct sufficient experiments to evaluate MIRGAN on two widely used datasets for chest-related diseases. The experimental results show that MIRGAN can significantly improve the performance of the generator on most metrics.
Joint Learning-based Multiple Documents Heterogeneous Graph Inference for Biomedical Entity Linking

Biomedical Entity Linking(BEL) is the task of linking biomedical mentions in natural language corpora such as diseases and drugs to standard entities in a given knowledge base. The same biomedical entity can have multiple mentions, including synonyms, morphological variations and names with different word order. Thus, making predictions for each mention with an insufficient context in a single biomedical document is challenging, especially when mentions or linked entities are unseen during training. This paper proposes an inference method for biomedical entity linking based on the heterogeneous graph constructed on multiple documents. We first design a joint representation learning method and compute mention-mention and mention-entity similarity in a unified semantic space. Based on them, we utilize the mutual nearest neighbors relationship between mentions and the relationship between mention and entity to construct a heterogeneous graph. Finally, we apply a clustering-based method to make linking predictions, which associates each mention in the documents with a unique entity. Extensive experiments on three biomedical entity linking benchmarks (MedMentions, BC5CDR and NCBI) demonstrate that our method outperforms other state-of-the-art entity linking models.
Machine learning based system for the automation of systematic literature reviews

The paper gives an overview of a machine learning-based system developed to support systematic literature reviews (SLR). The objective of the system is to provide scientists and anyone else who gives scientific advice supporting policy development with a tool for literature search and appraisal that reduces the human effort. The structure of the system is presented along with the description of the communication between modules and data storage methods. The Kafka technology is used for inter-module communication and the system consists of several independent modules which can be easily expanded with new modules without the need to introduce significant changes. We propose to semi–automate the SLR processes by applying an active learning approach which is based on machine learning classification models and on manual screening by experts of a subset of articles. Using classification algorithms requires a numerical representations of articles. This work investigates the utility of bag of concepts approach for text representations in order to create classification models used as components of automated systematic literature review systems. The presented study uses the bag of concepts approach in which a set of concepts identified by an annotator is extended by the concepts which lie, within a given distance, on paths from the originally identified concepts to the root of the ontology tree. Experiments are performed on datasets from systematic literature reviews in the medical domain. We summarize the performance of the proposed system by evaluating the WSS@95% metrics of active learning processes for several SLR case studies.
What will regularized continuous learning performs if it was used to medical image segmentation: a preliminary analysis

Regularization-based continuous learning (RCL) has been proven to be highly effective on struggling against catastrophic forgetting. However, its application in medical image segmentation (MIS) is relatively scarce. In this paper, we provide a unified description of 6 RCL methods (LwF, LwM, EWC, SI, MAS, and PIGWM) using Taylor expansion and investigate their performances in 2 classic MIS scenarios, namely retinal vessel segmentation (RVS) and cardiac left ventricle segmentation (CLVS) on 8 datasets (CHASE, DRHAGIS, RITE and STARE for the former and M&Ms, LVSC, ACDC and SCD for the latter). We also explore the influence of different task orders (easy to hard or hard to easy), optimizers (Adam or SGD), and parameter capacities (2, 3, 4 or 5 down- and up-sampling pairs) on the performance of these methods. Our experimental results show that these methods are capable of mitigating catastrophic forgetting to a certain extent. Comparing to a hard-to-easy order, most of the methods perform better on all of the already known tasks in an easy-to-hard order. Optimizer Adam performs better on RVS and CLVS. Capacity increases are obviously effective for CLVS, but they have no significant impact on RVS.
Data analytics framework for sparse longitudinal structured biomedical data

An increasing amount of data is stored in electronic health records originating from laboratory, imaging, and clinical examinations. However, the automated employment of machine learning algorithms for clinical decision tasks is still limited in the case of long-term medical structured data, such as the observations of patients suffering from multiple sclerosis, including numerical laboratory results and volumes derived from brain MRI segmentation. The main reason is the complexity of these data caused by high dimensionality, irregular temporal nature, and incompleteness in both time and observation dimensions.This study introduces a comprehensive automated framework designed for an end-to-end analysis of longitudinal structured biomedical data. It comprises a preprocessing component, which includes several methods for regularization and missing values imputation. Following, a prediction component suitable for various classification and regression tasks features a range of traditional machine learning and deep neural network models. Finally, the data visualization component based on the Potential of Heat-diffusion for Affinity-based Trajectory identifies the patterns in these complex data.Evaluation of this framework was conducted on a real-world dataset involving patients with multiple sclerosis, addressing tasks such as classifying the patient’s disability state and predicting the patient’s future disability score. Additionally, with the data visualization techniques, the study demonstrates that even incomplete long-term medical time series data can unveil valuable insights.
Interactive Ultrasound Prostate Cancer Segmentation using Deep Learning with Principal Curve-based Fine-tuning

Segmentation of prostate cancer on ultrasound (US) images is challenging because of the low-intensity contrast around the region of interest (ROI) contour caused by the intestinal gas. To the best of our knowledge, our work is one of the extremely rare groups to segment prostate cancer on US images, which designs a coarse-to-refinement segmentation framework with four merits: 1) it fuses the advantages of the deep learning- and principal curve-based algorithms to locate the ROI and fit the data center automatically, respectively; 2) a principal curve-based polygon searching method is designed by newly adding some constraints so that the performance of our method could be enhanced; 3) the quantum characteristics are added into evolution network, while newly adding numerous- operator scheme and global optimum search schemes; 4) an interpretable mathematical mapping model is developed to denote the ROI contour. Segmentation results show that our network outperforms other medical image segmentation models.
Synthesizing with Diffusion model for improving medical image segmentation performance

Diffusion probabilistic models (DPMs) have attracted much attention in the field of computer vision by demonstrating superior image generation capabilities. However, in the field of medical images, the scarcity of annotations often poses a challenge for the application of deep learning-like methods. Motivated by the success of DPMs, we present Medical Image Segmentation Augmentation Diffusion Model (MEDSAD), a DPM and mask-based medical image generation model designed for general medical image generation tasks to alleviate the problem of scarcity of images with annotation. MEDSAD leverages a simple annotation to generate paired medical images, thereby enhancing the model’s data exploration capabilities and improving segmentation performance in downstream segmentation tasks. To exert greater control over the texture generated from medical images in MEDSAD, we introduce the Texture Style Injection (TSI) mechanism, which restricts the model from generating textures rather than randomly. Additionally, we propose a Feature Frequency Domain Attention (FFDA) module to mitigate the adverse effects of high-frequency noise components during this process. We validate the performance enhancements achieved by MEDSAD on two medical segmentation tasks involving magnetic resonance (MR) and ultrasound (US) image modalities for breast tumor and brain tumor segmentation. The results demonstrate that MEDSAD outputted high-quantity synthetic pair medical images from given annotation, delivering the most substantial performance improvement even with a limited number of samples in the downstream segmentation task. These findings underscore the generalization and efficacy of the proposed model.
Pain Management and Conditioning through Virtual Reality and Affective Computing

This study investigates the real-world efficacy of tailored pain management methods, focusing on Virtual Reality (VR) distraction therapy — a technique utilising immersive VR experiences to divert attention from pain. Participants undergo two phases consisting of VR therapy with simultaneous physiological data collection and the delivering of a conditioning stimulus via a smartwatch. Utilising machine learning, the VR experience is able to adapt to emotional states, ensuring that an individual’s focus is shifted away from pain. Results reveal an overall 42% pain reduction, offering insights into diverse pain interventions.
HistDeblur: A Pyramid Trustworthy Framework for Blurry Histologic Artifacts Quality Control

To address the challenge of diagnosing histology images accurately, we propose a novel Transformer-based model, HistDeblur, to detect and eliminate blur in digitized microscope images. HistDeblur adopts a trustworthy deblurring pyramid architecture with a shared-weight structure for artifact restoration, ensuring image quality control. To the best of our knowledge, HistDeblur is the first attempt at a Transformer based model to detect and eliminate the blur in histology images. Uniquely, it incorporates an auxiliary branch in the decoder for explainable blurriness estimation, diverging from traditional pyramid models. A method is also introduced to synthesize non-uniform blurry images, simulating real-world artifacts occurring during specimen preparation and digitization. Experiments on a representative subset of CRC-VAL-HE-7K [1] show the proposed framework is superior to the state-of-the-art approaches in the context of histology deblurring by a margin of 1.97 PSNR and 0.03 SSIM respectively. HistDeblur, being fully automated, optimizes histology image classification, quality assessment, and restoration, thereby mimicking the histologist’s examination experience proficiently. The approach facilitates reliable medical diagnoses, allowing histologists to make informed decisions on accepting deblurred images or opting for costly resampling and re-scanning. The source code is available at https://github.com/KKK-Liu/HistDeblur
Causal Attention-Based Lightweight and Efficient Cervical Cancer Cell Detection Model

Cervical cancer, a prevalent malignancy among women, demands early screening and diagnosis for improved cure rates. Computer-aided screening systems offer accurate and rapid detection, reducing errors and enhancing efficiency. Convolutional neural networks have been extensively employed for cervical cancer cell detection in TCT images. However, these methods often overlook cervical cell morphology and intricate TCT image backgrounds, resulting in biased models. Moreover, their complexity limits practical application efficiency. Nevertheless, the causal attention mechanism can confine the model’s focus solely to the current morphology, preventing interference from other factors and effectively mitigating bias. And, lightweight strategies can enhance model efficiency and performance by reducing parameter count and computational demands. Thus, we introduce a lightweight high-performance model based on causal attention. Our cervical cancer cell detection model integrates causal attention-guided deformable convolutions in its backbone. This novel backbone improves CNN’s capacity to extract cellular morphological features, alleviating bias from target-background correlation. Additionally, a lightweight GSConv structure streamlines the neck architecture, further boosting efficiency. We also introduce a Grad-CAM-based explanation method for swift insight into model predictions. Experimental results demonstrate heightened precision, reducing parameters and computation by 20%, with a 1.9% average mAP increase. Our method reduces complexity and enhances precision in cervical cancer cell detection, showcasing its efficacy and potential in medical image object detection.
Interpretable Deep Learning Models With Concept Whitening Layers

Deep Learning models, such as convolutional neural networks (CNN), are hard to interpret due to their complex, nonlinear, and high-dimensional algorithms. We focused on interpreting CNNs for predicting Hepatocellular Carcinoma (HCC), the most common primary liver cancer, using histopathology images. We used a 50-layer residual neural network (RNN50), an inception CNN, and a recent innovation called concept whitening (CW). The results showed that adding CW layers to CNN models did not significantly affect model accuracy. However, post-hoc analysis methods, including inter-concept similarity rating, intra-concept similarity rating, concept importance rating, and feature vector displays, revealed improved interpretability.
MKFN: Multimodal Knowledge Fusion Network for Automatic ICD Coding

Automated International Classification of Diseases (ICD) coding tasks are designed to assign diagnosis and procedure codes to patients’ electronic medical records (EMRs). Recent works have applied deep neural network models and related techniques for code assignment to clinical notes. However, most existing methods have overlooked the advantageous complementary information presented in the tabular data from EMRs and the Wikipedia knowledge. Therefore, we propose a Multimodal Knowledge Fusion Network (MKFN) to effectively integrate clinical notes, tabular data, and Wikipedia knowledge, enhancing the model’s predictive capabilities. We incorporate structured tabular data and clinical notes into an initial multimodal representation using label attention and self-attention mechanisms. We propose a knowledge fusion network to leverage tabular data and Wikipedia knowledge for accurate predictions when code descriptions are absent in clinical notes. Experiments on the MIMIC dataset show that our proposed model achieves competitive results among existing ICD coding methods.
Proposal for a heuristic principle to evaluate interfaces of collaborative systems

The usability of interactive systems is nowadays considered a fundamental quality attribute of these systems, since when the user experience is negatively affected by an unusable interface, the user may decide not to use the system again.
Method for the Simultaneous Generation of Two Nonlinear Pseudo Random Sequences: 5-ary and Binary

Multi-level signals and sequences have become a significant aspect of modern high-speed communication systems. Hence, to ensure the confidentiality and integrity of the transmitted information, advanced methods and devices are necessary to produce strong cryptographic properties for not only binary but also for nonbinary keystreams, which can be used in resource-constrained microcontrollers. The proposed method and apparatus generate both a balanced nonlinear 5-ary pseudo-random sequence and a binary keystream sequence. The nonlinearity is determined by applying shrinking and multiplexing techniques to the generated linear 5-ary pseudo-random sequence and the running zero in its conversion to a binary balanced sequence.