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Self-supervised Pre-training Tasks for an fMRI Time-Series Transformer in Autism Detection 7th

Self-supervised Pre-training Tasks for an fMRI Time-Series...

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7th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Zhou, Yinchi Duan, Peiyu Du, Yuexi Dvornek, Nicha C. Department of Biomedical Engineering New HavenCT United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States

ISBN: (纸本)9783031787607

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that encompasses a wide variety of symptoms and degrees of impairment, which makes the diagnosis and treatment challenging. Functional magnetic resonance imaging (fMRI) has been extensively used to study brain activity in ASD, and machine learning methods have been applied to analyze resting state fMRI (rs-fMRI) data. However, fewer studies have explored the recent transformer-based models on rs-fMRI data. Given the superiority of transformer models in capturing long-range dependencies in sequence data, we have developed a transformer-based self-supervised framework that directly analyzes time-series fMRI data without computing functional connectivity. To address over-fitting in small datasets and enhance the model performance, we propose self-supervised pre-training tasks to reconstruct the randomly masked fMRI time-series data, investigating the effects of various masking strategies. We then fine-tune the model for the ASD classification task and evaluate it using two public datasets and five-fold cross-validation with different amounts of training data. The experiments show that randomly masking entire ROIs gives better model performance than randomly masking time points in the pre-training step, resulting in an average improvement of 10.8% for AUC and 9.3% for subject accuracy compared with the transformer model trained from scratch across different levels of training data availability. Our code is available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Analysis Pipeline for High-Dimensional Neuromechanical Model Improvement 13th

Analysis Pipeline for High-Dimensional Neuromechanical Model...

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13th international conference on Biomimetic and Biohybrid Systems, Living Machines 2024

作者： Fernandez, Camila J. McManus, Jeffrey M. Li, Yanjun Bennington, Michael J. Quinn, Roger D. Chiel, Hillel J. Webster-Wood, Victoria A. Department of Mechanical Carnegie Mellon University PittsburghPA15232 United States Department of Biomedical Engineering Carnegie Mellon University PittsburghPA15232 United States McGowan Institute for Regenerative Medicine Carnegie Mellon University PittsburghPA15232 United States Department of Biology Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Mechanical Engineering Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Neurosciences Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Biomedical Engineering Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States

ISBN: (纸本)9783031725968

To capture and understand animal behavior, engineers and biologists seek to develop biologically accurate neuromechanical models of muscle dynamics and neural control. However, demand-driven enhancement of complex neuromechanics, such as the multifunctional Aplysia californica feeding apparatus, can be challenging due to the multidimensional biomechanical and neural models involved. We propose an analysis pipeline that enables reinforcement learning (RL) to classify which aspects of an engineered neuromechanical model can accurately capture animal behavior. As an example, prioritizing where demand-driven enhancement of a biomechanical and neural model is needed, the neural model of a published neuromechanical model of Aplysia swallowing during feeding was replaced with an RL controller and their performances were compared and correlated with in vivo swallowing behavior. By comparing the performance of the neural model and the learned model to in vivo animal behavior, we can pinpoint areas for improvement. The analysis pipeline identified that the neuromechanical model confidently captured force performance with no significant difference from animal swallowing force behavior. It most usefully also indicated that the biomechanical model will need to be improved in future iterations to better capture motor neuron activity. Future work should explore the accuracy of the RL-enabled analysis pipeline with a more advanced biomechanical model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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Vehicle Detection Performance in Nordic Region 27th

Vehicle Detection Performance in Nordic Region

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27th international conference on Pattern Recognition, ICPR 2024

作者： Mokayed, Hamam Saini, Rajkumar Adewumi, Oluwatosin Alkhaled, Lama Backe, Björn Shivakumara, Palaiahnakote Hagner, Olle Hum, Yan Chai Electrical and Space Engineering Luleå University of Technology Luleå Sweden School of Science Engineering and Environment Salford University Salford United Kingdom Smartplanes Jävre Sweden Mechatronics and Biomedical Engineering Universiti Tunku Abdul Rahman Selangor Petaling Jaya Malaysia

ISBN: (纸本)9783031783111

This paper addresses the critical challenge of vehicle detection in the harsh winter conditions in the Nordic regions, characterized by heavy snowfall, reduced visibility, and low lighting. Due to their susceptibility to environmental distortions and occlusions, traditional vehicle detection methods have struggled in these adverse conditions. The advanced proposed deep learning architectures brought promise, yet the unique difficulties of detecting vehicles in Nordic winters remain inadequately addressed. This study uses the Nordic Vehicle Dataset (NVD), which contains UAV (unmanned aerial vehicle) images from northern Sweden, to evaluate the performance of state-of-the-art vehicle detection algorithms under challenging weather conditions. Our methodology includes a comprehensive evaluation of single-stage, two-stage, segmentation-based, and transformer-based detectors against the NVD. We propose a series of enhancements tailored to each detection framework, including data augmentation, hyperparameter tuning, transfer learning, and Specifically implementing and enhancing the Detection Transformer (DETR). A novel architecture is proposed that leverages self-attention mechanisms with the help of MSER (maximally stable extremal regions) and RST (Rough Set Theory) to identify and filter the region that model long-range dependencies and complex scene contexts. Our findings not only highlight the limitations of current detection systems in the Nordic environment but also offer promising directions for enhancing these algorithms for improved robustness and accuracy in vehicle detection amidst the complexities of winter landscapes. The code and the dataset are available at https://***. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Towards Biophysical Network Simulation of Stochastically-Formed Neurospheres 13th

Towards Biophysical Network Simulation of Stochastically-Fo...

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13th international conference on Biomimetic and Biohybrid Systems, Living Machines 2024

作者： Bennington, Michael J. Webster-Wood, Victoria A. Department of Mechanical Carnegie Mellon University PittsburghPA United States Department of Biomedical Engineering Carnegie Mellon University PittsburghPA United States McGowan Institute for Regenerative Medicine Carnegie Mellon University PittsburghPA United States Robotics Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9783031725968

Biocomputing platforms, such as cultured neurospheres, have the potential to provide great advances in biohybrid computation and control systems. However, to design and fabricate neurospheres reliably and reproducibly, models that can accurately predict their computational behaviors are required. Towards the end of understanding how neurospheres perform higher-level computations, we present a model framework for simulating the dynamics of stochastically-connected neuron networks. Each neuron is modeled using biophysical models of neural excitability, and the system can be stimulated by a small number of simulated electrodes. The network response of the system is analyzed using Principal Components Analysis on the firing frequencies of the neurons. From preliminary simulations, we demonstrate the ability of these neurosphere networks to encode information about the magnitude of current stimuli. Future additions to this framework will incorporate the 3D geometry of both the neurosphere and individual dendritic trees. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stochastic models

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A Supervised Segmentation Solution: Diabetic Foot Ulcers Challenge 2024 4th

A Supervised Segmentation Solution: Diabetic Foot Ulcers Cha...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Feng, Xue Huang, Gaofeng Department of Biomedical Engineering University of Virginia CharlottesvilleVA United States Carina Medical LLC LexingtonKY United States

ISBN: (纸本)9783031808708

This paper presents a purely supervised solution for the DFUC2024 challenge without using the un-annotated data, focusing on the segmentation of diabetic foot ulcers (DFUs) using deep learning techniques. We compared two prominent architectures, UNet and DeepLabV3, evaluating their performance using a 5-fold cross-validation (CV) strategy. The DeepLabV3 model, leveraging a ResNet50 backbone pretrained on ImageNet, outperformed the UNet model, achieving an average Dice Coefficient Score (DSC) of 0.7909 across the CV sets. Despite these results, the model demonstrated overfitting, with a reduced DSC of 0.6751 on the challenge test set, highlighting the challenges of generalization in data-limited scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Classification of Cutaneous Diseases: A Systematic Study on Real-Time Captured Images Using Deep Learning 27th

Classification of Cutaneous Diseases: A Systematic Study o...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Kanekar, Bhavik Sawant, Jay Chikhale, Niti Dhotre, Paras Savant, Sushil Nagare, Gajanan Jadhav, Kshitij Koita Centre for Digital Health Indian Institute of Technology Bombay Mumbai India Department of Biomedical Engineering Vidyalankar Institute of Technology Mumbai India The Humanitarian Clinic Mumbai India

ISBN: (纸本)9783031782008

Dermatological infectious diseases pose a significant public health concern due to their highly contagious nature, often characterized by painful sores, fluid-filled blisters, flesh-colored bumps, and itching. Despite their distinct visual symptoms, diagnosing these diseases can be challenging due to their phenotypical similarities and overlapping clinical presentations with other skin conditions. In this paper we introduce a novel and diverse skin lesion dataset comprising patients from India, focusing on prevalent infectious skin conditions such as herpes zoster, herpes simplex, molluscum contagiosum, and non-viral skin disorders. These conditions are particularly common in this geographical region. Furthermore, we test Deep Learning models using different augmentation techniques, analyze the performance, and evaluate several metrics on different deep models using image augmentations. By assessing the performance of these models and analyzing several metrics across different augmentation methods, our findings demonstrate the capability of deep-learning models in classifying skin images and such computational techniques can be used to enhance healthcare accessibility and effectiveness, in resource-constrained settings like India. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Integrated Grading Framework for Histopathological Breast Cancer: Multi-level Vision Transformers, Textural Features, and Fusion Probability Network 27th

Integrated Grading Framework for Histopathological Breast C...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Balaha, Hossam Magdy Ali, Khadiga M. Mahmoud, Ali Ghazal, Mohammed El-Baz, Ayman Department of Bioengineering J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Pathology Department Faculty of Medicine Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781032

Breast cancer (BC) remains a significant global health concern, necessitating accurate and efficient diagnostic approaches. In this study, we propose a comprehensive framework that integrates feature extraction, selection, and classification using Support Vector Machines (SVM) along with hyperparameter optimization. Additionally, we employ multi-level Vision Transformers (ViTs) for patch classification, aiming to capture both local and global information from histopathological slides. Moreover, we introduce a Fusion Probability Network (FPN) to combine the outputs of SVM and ViTs. Through this multi-faceted approach, we aim to improve diagnostic performance and contribute to more effective BC diagnosis. Sensitivity analyses and ablation studies across various sample sizes confirm the framework’s effectiveness. Results show high accuracy (up to 96.50%), precision (up to 93.33%), recall (up to 93%), specificity (up to 97.67%), F1 score (up to 92.99%), and balanced accuracy (up to 95.33%). Ablation studies highlight the significance of the feature extraction pipeline in enhancing the framework’s effectiveness and robustness, as well as its adaptability to diverse patch morphologies. Overall, our study offers promising avenues for improving BC grading, with potential implications for enhancing clinical decision-making and patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Self-supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation 4th

Self-supervised Instance Segmentation of Diabetic Foot Ulce...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Zhang, Wenhui Banerjee, Abhirup Ray, Surajit School of Mathematics and Statistics University of Glasgow GlasgowG12 8QQ United Kingdom Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom

ISBN: (纸本)9783031808708

Diabetic foot ulcers (DFUs) are a serious complication of diabetes that can often lead to infection, amputation, and even death if not properly managed. Accurate segmentation of DFUs in medical images is crucial for effective treatment planning. In the DFUC2024 challenge, which emphasizes self-supervised learning techniques for DFU segmentation, we investigate two approaches. The first approach utilizes a DINO (self-distillation with no labels) model combined with a trainable clustering probe to map unsupervised features into discrete segmentation labels. The second approach involves modifying the STEGO model, specifically designed to distill unsupervised features into meaningful segmentation labels, by integrating self-attention features from the ViT backbone to enhance spatial information. To further improve segmentation accuracy, we propose a coarse-to-fine instance prediction framework, where initial coarse predictions are refined through focused reprocessing of detected ulcer regions. After optimizing the hyperparameters for the DFU dataset, the modified STEGO model achieves a Dice coefficient of 0.4362 and Jaccard coefficient of 0.3358. Although the proposed approach yields competitive results, the challenge of self-supervision in DFU segmentation remains significant. The implementation of this work is available at https://***/Wenhui-Zhang-5/DFUC2024-challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Harnessing Vision Transformers for Precise and Explainable Breast Cancer Diagnosis 27th

Harnessing Vision Transformers for Precise and Explainable B...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Balaha, Hossam Magdy Ali, Khadiga M. Gondim, Dibson Ghazal, Mohammed El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Pathology Department Faculty of Medicine Mansoura University Mansoura Egypt Department of Pathology and Laboratory Medicine University of Louisville LouisvilleKY United States Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781940

Breast cancer (BC) remains a significant global health challenge, impacting millions of lives annually. Traditional histopathological analysis, while essential, can be subjective and time-consuming, potentially leading to diagnostic inaccuracies. This study proposes a novel Computer-Aided Diagnosis (CAD) framework utilizing Vision Transformers (ViTs) for BC diagnosis from histopathology slides. ViTs excel in capturing global dependencies within images, offering enhanced diagnostic accuracy compared to conventional methods. The framework integrates ViTs with advanced decision-making techniques like 2-tier majority fusion and SHapley Additive exPlanations (SHAP) for improved interpretability. Experimental results on a dataset of post-neoadjuvant therapy breast cancer samples demonstrate the efficacy of the proposed approach, achieving high performance metrics and providing insights into model predictions. The proposed approach achieves state-of-the-art performance with an accuracy exceeding 97% surpassing existing methods both on the utilized dataset and an external benchmark, specifically the Breast Cancer Histopathological Database (BreakHis). Time complexity analysis suggests that the proposed framework offers computational efficiency, with the dominant factors influencing overall complexity being the number of patches, sequence length, and number of layers in the ViT model. This study contributes a robust methodology towards enhancing BC diagnostic precision and efficiency through cutting-edge AI technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Multi-stage Segmentation of Diabetic Foot Ulcers Using Self-supervised Learning 4th

Multi-stage Segmentation of Diabetic Foot Ulcers Using Self...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Gupta, Neetika Nangia, Ayush Khan, Naimul Mefraz Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9783031808708

Diabetic Foot Ulcers (DFUs) pose a significant healthcare challenge due to their potential to lead to severe complications, including infections and ischemia, often necessitating limb amputation or causing mortality. Accurate diagnosis of ulcer regions, and prompt response can prevent amputations and fatalities through early and reliable DFU detection. However, manual examination of DFUs is labor-intensive and prone to variability. In response to these challenges, we propose DFUC_CAM, a novel method for instance segmentation of DFUs using self-supervised learning (SSL). The proposed approach leverages a multi-stage methodology. Initially, a convolutional neural network (CNN) trained for classification is utilized to generate Class Activation Maps (CAMs), which localize and highlight discriminative regions within DFU images. These CAMs serve as pseudo-labels to train a segmentation network using a SSL framework. For the MICCAI 2024 Diabetic Foot Ulcer Challenge (DFUC 2024), we trained DFUC_CAM using the DFUC 2024 and FUSeg datasets. The FUSeg dataset primarily serves for the quantitative evaluation of our method. During the validation phase of DFUC 2024, our team, ***, achieved a mean Dice score of 0.342, and in the final testing phase, we improved this to 0.385. These results demonstrate the potential of our approach for enhancing DFU detection and segmentation in clinical settings. The code is available at: https://***/RyersonMultimediaLab/DFUC_CAM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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