Medical image segmentation models struggle to achieve efficient global context modeling and long-range dependency reasoning under practical computational budgets. In this work, we propose a hybrid architecture utilizing U-Mamba with Heat Conduction Equation, which combines state-space modules for efficient long-range reasoning with Heat Conduction Operators (HCOs) in the bottleneck layers, simulating frequency-domain thermal diffusion for enhanced semantic abstraction. Experimental results show that our model attains the highest DSC (0.8719) on the Abdomen CT dataset. It suggests that blending state-space dynamics with heat-based global diffusion offers a scalable solution for medical segmentation tasks.
SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）
Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
Enhancing Cross-Modal Medical Image Segmentation through Compositionality
University of Amsterdam（阿姆斯特丹大学）；The Netherlands Cancer Institute（荷兰癌症研究所）；Utrecht University（乌得勒支大学）；Maastricht University Medical Center（马斯特里赫特大学医学中心）
本文提出一种端到端跨模态医学图像分割框架，通过引入可学习的 von Mises-Fisher 核实现组合性归纳偏置与内容-风格解耦，有效提升了分割性能、可解释性并降低计算成本。
Object segmentation plays an important role in the modern medical image analysis, which benefits clinical study, disease diagnosis, and surgery planning. Given the various modalities of medical images, the automated or semi-automated segmentation approaches have been used to identify and parse organs, bones, tumors, and other regions-of-interest (ROI). However, these contemporary segmentation approaches tend to fail to predict the boundary areas of ROI, because of the fuzzy appearance contrast caused during the imaging procedure. To further improve the segmentation quality of boundary areas, w…
DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation
Faculty of Military Health Service, Naval Medical University, China（海军医学院军事卫生服务学院）；School of Computer Science and Technology, Shanghai University of Electric Power, China（上海电力大学计算机科学与技术学院）
RWTH Aachen University（亚琛工业大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；Mila, Quebec AI Institute（米拉魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）；Polytechnique Montreal（蒙特利尔理工学院）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-sha…
The shapes and morphology of the organs and tissues are important prior knowledge in medical imaging recognition and segmentation. The morphological operation is a well-known method for morphological feature extraction. As the morphological operation is performed well in hand-crafted image segmentation techniques, it is also promising to design an approach to approximate morphological operation in the convolutional networks. However, using the traditional convolutional neural network as a black-box is usually hard to specify the morphological operation action. Here, we introduced a 3D morpholo…
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong（香港中文大学深圳大数据研究院）；Sun Yat-sen University（中山大学）；Pazhou Lab（琶洲实验室）
Medical vision-and-language pre-training (Med-VLP) has received considerable attention owing to its applicability to extracting generic vision-and-language representations from medical images and texts. Most existing methods mainly contain three elements: uni-modal encoders (i.e., a vision encoder and a language encoder), a multi-modal fusion module, and pretext tasks, with few studies considering the importance of medical domain expert knowledge and explicitly exploiting such knowledge to facilitate Med-VLP. Although there exist knowledge-enhanced vision-and-language pre-training (VLP) method…
TG-LMM: Enhancing Medical Image Segmentation Accuracy through Text-Guided Large Multi-Modal Model
Sun Yat-sen University（中山大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家肿瘤临床医学研究中心/肿瘤医院&深圳医院）；University of Macau（澳门大学）
The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery. Most existing methods require a fully labeled dataset in each source domain. Although some researchers developed a semi-supervised domain generalized method, it still requires the domain labels. This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. The main goal is to enhance the pseudo label quality for unlabeled images from unknown distributions. To achieve…
Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China, 541004（桂林电子科技大学人工智能学院）；The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montreal, Canada, H3C 1K3（影像、视觉与人工智能实验室）；College of Medicine, Taibah University, Al Madinah, Saudi Arabia, 42353（塔布大学医学院）；Department of Radiology, Prince Mohammed Bin Abdulaziz Hospital, Ministry of National Guard Health Affairs, Al Madinah, Saudi Arabia, 42324（王子穆罕默德·本·阿卜杜勒阿齐兹医院放射科）；College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia, 42353（塔布大学计算机科学与工程学院）；College of Computer Science and Engineering, Jeddah University, Jeddah, Saudi Arabia, 23445（吉达大学计算机科学与工程学院）
Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation
Northwestern Polytechnical University（西北工业大学）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）；Research and Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）
GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation
Computer Science Department Michigan State University East Lansing, USA（密歇根州立大学计算机科学系）；Department of Radiation Oncology Henry Ford Health Detroit, US（亨利福特健康辐射肿瘤科）；Division of Engineering New York University Abu Dhabi, UAE（纽约大学工程学院）
UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTM
Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, USA（密歇根大学电气工程与计算机科学系）；Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, USA（佐治亚理工学院和埃默里大学沃森·霍尔生物医学工程系）；Computer Science Department, University of Michigan, Ann Arbor, USA（密歇根大学计算机科学系）；Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院沃森·霍尔生物医学工程系）；Emory University, Atlanta, USA（埃默里大学）
Imperial College London（帝国理工学院）；University of Edinburgh（爱丁堡大学）；Fudan University（复旦大学）；City, University of London（伦敦城市大学）；Technical University of Munich（慕尼黑工业大学）
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation tasks. AdvChain augments data with dynamic data augmentation, generating randomly chained photo-met…
Tree-NET: Enhancing 2D Medical Image Segmentation Through Efficient Low-Level Feature Training
Abdullah Gul University（阿卜杜拉·居尔大学）；Hacettepe University（哈杰泰佩大学）；Gulf University for Science and Technology (GUST)（海湾科技大学）；GUST Engineering and Applied Innovation Research Center (GEAR)（GUST工程与应用创新研究中心）
Perspective+ Unet: Enhancing Segmentation with Bi-Path Fusion and Efficient Non-Local Attention for Superior Receptive Fields
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）
The visual analysis of retina and of its vascular characteristics is important in the diagnosis and monitoring of diseases of visual perception. In the related medical diagnoses, the digital processing of the fundus images is used to obtain the segmentation of retinal vessels. However, an image segmentation is often requiring methods based on peculiar or complex algorithms: in this paper we will show some alternative approaches obtained by applying freely available tools to enhance, without a specific segmentation, the images of the fundus of the eye. We will see in particular, that combining …
Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Peng Cheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））
Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data
Columbia University（哥伦比亚大学）；Texas Tech University（德克萨斯理工大学）；University of California, Davis（加利福尼亚大学戴维斯分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；North Carolina State University（北卡罗来纳州立大学）
VinBigData JSC（VinBigData股份公司）；VinUniversity（VinUniversity（温纳大学））；VinUni-Illinois Smart Health Center（VinUni-伊利诺伊智慧健康中心）
Building an accurate computer-aided diagnosis system based on data-driven approaches requires a large amount of high-quality labeled data. In medical imaging analysis, multiple expert annotators often produce subjective estimates about "ground truth labels" during the annotation process, depending on their expertise and experience. As a result, the labeled data may contain a variety of human biases with a high rate of disagreement among annotators, which significantly affect the performance of supervised machine learning algorithms. To tackle this challenge, we propose a simple yet effective a…
Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models
The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xidian University, China（陕西省智能人机交互与可穿戴技术重点实验室，西安电子科技大学）；Warren Alpert Medical School, Brown University, USA（布朗大学沃伦·阿尔珀特医学院）；National University of Singapore, Singapore（新加坡国立大学）
Enhancing Medical Visual Grounding via Knowledge-guided Spatial Prompts
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Yong Yoo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Institute of High-Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）
Enhancing medical vision-language contrastive learning via inter-matching relation modelling
the University of Sydney（悉尼大学）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Shanghai Jiao Tong University（上海交通大学）；Institute of Translational Medicine（转化医学研究院）
Perceive and Calibrate: Analyzing and Enhancing Robustness of Medical Multi-Modal Large Language Models
Department of Computer Science and Engineering, CUHK, Hong Kong, China（计算机科学与工程系，CUHK，香港，中国）；Institute of Medical Intelligence and XR, CUHK, Hong Kong, China（医学智能与XR研究所，CUHK，香港，中国）
SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting
J. Crayton Pruitt Family Department of Biomedical Engineering University of Florida；Department of Electrical \& Computer Engineering University of Florida
Although supervised deep-learning has achieved promising performance in medical image segmentation, many methods cannot generalize well on unseen data, limiting their real-world applicability. To address this problem, we propose a deep learning-based Bayesian framework, which jointly models image and label statistics, utilizing the domain-irrelevant contour of a medical image for segmentation. Specifically, we first decompose an image into components of contour and basis. Then, we model the expected label as a variable only related to the contour. Finally, we develop a variational Bayesian fra…
