In recent years, high-performance computer vision models have achieved remarkable success in medical imaging, with some skin lesion classification systems even surpassing dermatology specialists in diagnostic accuracy. However, such models are computationally intensive and large in size, making them unsuitable for deployment on edge devices. In addition, strict privacy constraints hinder centralized data management, motivating the adoption of Federated Learning (FL). To address these challenges, this study proposes a skewness-guided pruning method that selectively prunes the Multi-Head Self-Attention and Multi-Layer Perceptron layers of a multimodal Swin Transformer based on the statistical skewness of their output distributions. The proposed method was validated in a horizontal FL environment and shown to maintain performance while substantially reducing model complexity. Experiments on the compact Swin Transformer demonstrate approximately 36\% model size reduction with no loss in accuracy. These findings highlight the feasibility of achieving efficient model compression and privacy-preserving distributed learning for multimodal medical AI on edge devices.
Mask-Guided Multi-Channel SwinUNETR Framework for Robust MRI Classification
Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain（巴塞罗那大学数学与计算机系）；Computer Vision Center, Bellaterra, Spain（贝尔特拉拉计算机视觉中心）；Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain（加泰罗尼亚高级研究与教育研究所）
Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification
Sungkyunkwan University（成均馆大学）；College of Computing and Informatics, Sungkyunkwan University（成均馆大学计算与信息学院）；Dept. of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电气与计算机工程系）
SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention
School of Physics and Information Technology（物理与信息学院）；Shaanxi Normal University（陕西师范大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Central South University（中南大学）
EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；Xi'an Jiaotong-Liverpool University（西交利物浦大学）；University of Leicester（莱斯特大学）；Southeast University（东南大学）；University of Liverpool（利物浦大学）
SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection
College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；The First Affiliated Hospital, Sun Yat-Sen University（中山大学附属第一医院）；Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University（华南师范大学阿伯丁数据科学与人工智能学院）
Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University（麻醉学、围术期与疼痛医学系，斯坦福大学）；Department of Biomedical Sciences, Seoul National University（生物医学科学系，首尔国立大学）；Department of Computer Engineering, National University of Sciences and Technology (NUST)（计算机工程系，国立科学与技术大学（NUST））；Department of Computer Science, The University of Alabama（计算机科学系，阿拉巴马大学）；Department of Computer Science, Hanyang University（计算机科学系，翰阳大学）
Background and objective: Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors are critical. However, the relative rarity of these tumors and the highly dispersed tissue types have left an unmet need for a subtle differential diagnosis of such neoplastic lesions based on preoperative radiomics. Recently, deep learning methods have developed rapidly, especially Transformer beats the traditional convolutional neural network in computer v…
Shahid Beheshti University（沙希德·贝赫什提大学）；RWTH Aachen University（亚琛工业大学）；Alzahra University（阿尔扎赫拉大学）；University of Regensburg（雷根斯堡大学）
Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diagnosis by automatic skin lesion segmentation, several Fully Convolutional Network (FCN) approaches, specifically the U-Net architecture, have been proposed. The U-Net model with a symmetrical architecture has exhibited superior performance in the segmentation task. However, the locality restriction of the convolutional operation incorporated in the U-Net architecture limits its performance in capturing long-range depe…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-superv…
Fusion of Foundation and Vision Transformer Model Features for Dermatoscopic Image Classification
Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University（医学影像分析与人工智能研究中心、医学部、医学与牙科学院、多瑙私人大学）；Department of Research and Development, TissueGnostics GmbH（研发部、TissueGnostics GmbH）
HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation
school of Mathematics and Statistics, Changsha University of Science and Technology（长沙理工大学数学与统计学院）；school of Computer and Communication Engineering, Changsha University of Science and Technology（长沙理工大学计算机与通信工程学院）
Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification
Danube Private University (DPU)（多瑙河私立大学）；Austrian Centre for Medical Innovation and Technology (ACMIT)（奥地利医学创新与技术中心）；Medical University of Vienna（维也纳医科大学）；Urmia University of Medical Science（乌尔米亚医科大学）
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
University of Washington（华盛顿大学）；Paul G. Allen School of Computer Science, University of Washington（华盛顿大学保罗·G·艾伦计算机科学学院）；Department of Neurological Surgery, University of Washington（华盛顿大学神经外科（注：此处按要求提取到学院/大学级别，实际原文该机构属于华盛顿大学，故提取为华盛顿大学））
BreastSegNet: Multi-label Segmentation of Breast MRI
Department of Biostatistics \& Bioinformatics, Duke University Department of Electrical；Computer Engineering, Duke University Department of Radiology, Duke University Medical Center Department of Computer Science, Duke University
Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat（斯瓦特工程与应用科学大学计算机系统工程系人工智能实验室）；Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran（法赫德国王石油矿产大学智能移动与物流跨学科研究中心）
Duke University（杜克大学）；Vanderbilt University（范德堡大学）
Lesion detection in digital breast tomosynthesis (DBT) is an important and a challenging problem characterized by a low prevalence of images containing tumors. Due to the label scarcity problem, large deep learning models and computationally intensive algorithms are likely to fail when applied to this task. In this paper, we present a practical yet lightweight backbone to improve the accuracy of tumor detection. Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images a…
HyFormer-Net: A Synergistic CNN-Transformer with Interpretable Multi-Scale Fusion for Breast Lesion Segmentation and Classification in Ultrasound Images
Department of Computer Science and Engineering, Ahsanullah University of Science and Technology（计算机科学与工程系，阿沙努尔大学科学与技术学院）
Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis
Department of Computer Science and Engineering, European University of Bangladesh（计算机科学与工程系，孟加拉国欧洲大学）；Department of Computer Science and Engineering, BRAC University（计算机科学与工程系，BRAC大学）；Department of Information Systems, University of Maryland, Baltimore County（信息系统系，马里兰大学巴尔的摩县分校）
TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection
VinUni-Illinois Smart Health Center, VinUniversity（VinUni-Illinois智慧健康中心，VinUniversity）；College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）
A Novel Hybrid Deep Learning and Chaotic Dynamics Approach for Thyroid Cancer Classification
Laboratory of Renewable Energy, Department of Electronics, University of Jijel（可再生能源实验室，电子系，杰吉尔大学）；Non-Destructive Testing Laboratory, Department of Electronics, University of Jijel（无损检测实验室，电子系，杰吉尔大学）；Institute of Technology, University Center Salhi Ahmed（技术学院，Salhi Ahmed大学中心）；College of Engineering and Information Technology, University of Dubai（工程与信息科技学院，迪拜大学）
Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation
School of Computation, Information and Technology, Technical University Munich（技术大学慕尼黑计算、信息与技术学院）；Department of Radiation Oncology, TUM School of Medicine, TUM University Hospital rechts der Isar, Technical University of Munich（肿瘤放射治疗科，TUM医学院，TUM大学医院rechts der Isar，技术大学慕尼黑）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（生物医学影像中的机器学习研究所，海德堡慕尼黑）；Institute of Radiation Medicine, Helmholtz Center Munich（放射医学研究所，海德堡慕尼黑）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）；Departament de Matemàtiques i Informàtica, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona（数学与计算机科学系，巴塞罗那人工智能在医学实验室（BCN-AIM），巴塞罗那大学）
