Medical image segmentation is vital for clinical diagnosis and quantitative analysis, yet remains challenging due to the heterogeneity of imaging modalities and the high cost of pixel-level annotations. Although general interactive segmentation models like SAM have achieved remarkable progress, their transfer to medical imaging still faces two key bottlenecks: (i) the lack of adaptive mechanisms for modality- and anatomy-specific tasks, which limits generalization in out-of-distribution medical scenarios; and (ii) current medical adaptation methods fine-tune on large, heterogeneous datasets without selection, leading to noisy supervision, higher cost, and negative transfer. To address these issues, we propose SegMoTE, an efficient and adaptive framework for medical image segmentation. SegMoTE preserves SAM's original prompt interface, efficient inference, and zero-shot generalization while introducing only a small number of learnable parameters to dynamically adapt across modalities and tasks. In addition, we design a progressive prompt tokenization mechanism that enables fully automatic segmentation, significantly reducing annotation dependence. Trained on MedSeg-HQ, a curated dataset less than 1% of existing large-scale datasets, SegMoTE achieves SOTA performance across diverse imaging modalities and anatomical tasks. It represents the first efficient, robust, and scalable adaptation of general segmentation models to the medical domain under extremely low annotation cost, advancing the practical deployment of foundation vision models in clinical applications.
Low-Rank Mixture-of-Experts for Continual Medical Image Segmentation
Peking University（北京大学）；Institute of Medical Technology, Peking University（北京大学医学技术研究所）；Institute of Biomedical Engineering, Peking University Shenzhen Graduate School（北京大学深圳研究生院生物医学工程研究所）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw（华沙大学数学、信息学与力学系）；Faculty of Mathematics and Computer Science, Jagiellonian University（雅盖隆大学数学与计算机科学系）；Institute of AI for Health, Helmholtz Munich（海德堡医学院人工智能与健康研究所）；Faculty of Electronics and Information Technology, Warsaw University of Technology（华沙理工大学电子与信息技术系）；NASK - National Research Institute（国家研究 institute）；Faculty of Radiology, Massachusetts General Hospital（麻省总医院放射学系）；Department of Radiology, Harvard Medical School（哈佛医学院放射学系）
Intelligent Communication Mixture-of-Experts Boosted-Medical Image Segmentation Foundation Model
College of Optoelectronics Engineering, Chongqing University, Chongqing, China；Center of Digital Dentistry, Peking University School；Hospital of Stomatology \& NHC Key Laboratory of Digital Stomatology, Beijing, China
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；College of Computer Science, East China Normal University（华东师范大学计算机科学学院）；Department of Data Science and AI, Faculty of IT, Monash University（莫纳什大学信息技术学院数据科学与人工智能系）；College of Computer Science, Sichuan University（四川大学计算机学院）
Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-demanding, especially for medical images. To alleviate this problem, we propose to utilize solely scribble annotations for weakly supervised segmentation. Existing solutions mainly leverage selective losses computed solely on annotated areas and generate pseudo gold standard segmentation by propagating labels to adjacent areas. However, these methods could suffer from the inaccurate and sometimes unrealistic pseudo segmentation due to the insufficient supervision and incomplete shape features. Different f…
Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation
Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）；Imperial Global Singapore (IGS), Imperial College London（伦敦帝国理工学院新加坡帝国全球（IGS））
Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部生物医学工程学院）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州研究院）；Army Medical University (Third Military Medical University)（中国人民解放军陆军军医大学（第三军医大学））；The First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；University of Science and Technology of China（中国科学技术大学）
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification
School of Electrical Engineering（电气工程学院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Department of Software Convergence（软件融合系）；Seoul Women’s University（首尔女子大学）
MicarVLMoE: A Modern Gated Cross-Aligned Vision-Language Mixture of Experts Model for Medical Image Captioning and Report Generation
Faculty of Computer Science and Information Technology, Universiti Malaya（马来亚大学计算机科学与信息学院）；School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算机与信息学院）
Wellcome Centre for Human Neuroimaging（惠康人类神经影像中心）；UCL（伦敦大学学院）；UCL Institute of Neurology（伦敦大学学院神经学研究所）
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpretable generative modelling approach to medical image translation. By allowing a common model for group-wise normalisation and segmentation of brain scans to handle missing data, the model allows for predicting entirely missing modalities from one, or a few, MR contrasts. Furthermore, the model can be trained on a fairly small number of subjects. The proposed model is validated on three clinically relevant scenarios. …
Identify the cells' nuclei is the important point for most medical analyses. To assist doctors finding the accurate cell' nuclei location automatically is highly demanded in the clinical practice. Recently, fully convolutional neural network (FCNs) serve as the back-bone in many image segmentation, like liver and tumer segmentation in medical field, human body block in technical filed. The cells' nuclei identification task is also kind of image segmentation. To achieve this, we prefer to use deep learning algorithms. we construct three general frameworks, one is Mask Region-based Convolutional…
National Institutes of Health（美国国立卫生研究院）；University of Wisconsin（威斯康星大学）
Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image analysis tools are in high demand since medical images from different domains are often needed to achieve a precise diagnosis. An important example in radiology is generalizing from non-contrast CT to contrast enhanced CTs. Contrast enhanced CT scans at different phases are used to enhance certain pathologies or organs. Many existing cross-domain image-to-image translation models have been shown to improve cross-domai…
Information Bottleneck-based Causal Attention for Multi-label Medical Image Recognition
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR)（联合山东大学-东北大学人工智能研究中心（C-FAIR））；Shandong University（山东大学）；School of Information Science and Engineering（信息科学与工程学院）；Child Care Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属儿童医院）；Case Western Reserve University（凯斯西储大学）
Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Experts
Harvard Medical School and the Brigham and Women’s Hospital（哈佛医学院和布里洛妇产科医院）；MIND Team, Inria Saclay, Université Paris-Saclay, Palaiseau, France and Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière（MIND团队、Inria Saclay、巴黎萨克雷大学、Palaiseau，法国以及索邦大学、巴黎脑研究所-ICM、CNRS、Inria、Inserm、AP-HP、皮蒂埃萨勒普蒂耶医院）；Massachusetts Institute of Technology（麻省理工学院）
Specializing Foundation Models via Mixture of Low-Rank Experts for Comprehensive Head CT Analysis
Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ USA（西门子医疗数字化技术与创新部）；Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany（西门子医疗计算机断层扫描部）；Department of Radiology, New York University, New York, NY USA（纽约大学放射科部）；Department of Radiology, University of Maryland Medical Center, Baltimore, MD USA（马里兰大学医学中心放射科部）；Department of Radiology, Vancouver General Hospital, Vancouver, BC Canada（温哥华总医院放射科部）；Department of Radiology, Northwell Health, New York, NY USA（北well医疗放射科部）；Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, CO USA（UCHealth纪念医院外科部）
University of Electronic Science and Technology of China（电子科技大学）；Tencent Jarvis Lab（腾讯 Jarvis 实验室）
Medical image segmentation has achieved remarkable advancements using deep neural networks (DNNs). However, DNNs often need big amounts of data and annotations for training, both of which can be difficult and costly to obtain. In this work, we propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML). Unlike most existing methods which only deal with the lack of annotations while assuming abundance of data, our framework works with extreme scarcity of both, which is ideal for rare diseases. Via DML, the framework…
BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming
Guangdong Institute of Intelligence Science and Technology（广东智能科技研究院）；Zhejiang University（浙江大学）；Southeast University（东南大学）；The Hong Kong Polytechnic University（香港理工大学）；Institute of Science Tokyo（东京科学大学）；Shanghai Jiao Tong University（上海交通大学）
University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Quantitative medical image computing (radiomics) has been widely applied to build prediction models from medical images. However, overfitting is a significant issue in conventional radiomics, where a large number of radiomic features are directly used to train and test models that predict genotypes or clinical outcomes. In order to tackle this problem, we propose an unsupervised learning pipeline composed of an autoencoder for representation learning of radiomic features and a Gaussian mixture model based on minimum message length criterion for clustering. By incorporating probabilistic modeli…
Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation
Center for Advanced Medical Computing and Analysis (CAMCA), Department of Radiology, Massachusetts General Hospital (MGH) and Harvard Medical School（先进医学计算与分析中心（CAMCA）、放射科、麻省总医院（MGH）和哈佛医学院）；Department of Radiation Oncology, Yonsei University College of Medicine（燕京大学医学院放射肿瘤科）；Institute for Innovation in Digital Healthcare, Yonsei University（数字医疗创新研究所、燕京大学）；Department of Radiation Oncology, Massachusetts General Hospital（麻省总医院放射肿瘤科）；Department of Radiation Oncology, Gangnam Severance Hospital（江南松云医院放射肿瘤科）；Department of Radiation Oncology, Yongin Severance Hospital（永兴松云医院放射肿瘤科）；School of Computing, University of Georgia（佐治亚大学计算机学院）；Department of Radiation Oncology, Mayo Clinic（梅奥诊所放射肿瘤科）；Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology（金 Jaechul人工智能研究生院、韩国科学技术院）
Early Intervention for VFM-based Multimodal Medical Image Classification
Renmin University of China（中国人民大学）；Beijing Key Laboratory for Intelligent Diagnosis of Fundus Diseases and Drug-Device R&D and Translation（眼底疾病智能诊断与药械研发转化北京市重点实验室）
本文提出EI框架，通过早期干预提升多模态影像疾病识别性能，引入MoR方法优化Vision Foundation Models适应，验证了在三个公开数据集上的有效性。
M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model
School of Computer Science and Technique（计算机科学与技术学院）；Tongji University（同济大学）；School of Communications and Electronic Engineering（通讯与电子工程学院）；East China Normal University（华东师范大学）
Recent research on formal verification for Collective Adaptive Systems (CAS) pushed advancements in spatial and spatio-temporal model checking, and as a side result provided novel image analysis methodologies, rooted in logical methods for topological spaces. Medical Imaging (MI) is a field where such technologies show potential for ground-breaking innovation. In this position paper, we present a preliminary investigation centred on applications of spatial model checking to MI. The focus is shifted from pure logics to a mixture of logical, statistical and algorithmic approaches, driven by the …
SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixup
Yong Loo Lin School of Medicine, National University of Singapore（杨潞龄医学院，新加坡国立大学）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；ETH Zurich（苏黎世联邦理工学院）
MedCoT: Medical Chain of Thought via Hierarchical Expert
ZJU-Angelalign R&D Center for Intelligence Healthcare, Zhejiang University（浙江大学ZJU-Angelalign智能医疗研发中心）；Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, France（斯特拉斯堡大学、法国国家科学研究中心、法国国家医学研究院、ICube、UMR7357、法国）；University Hospital of Strasbourg, France（斯特拉斯堡大学医院、法国）
pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation
College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；Chinese Academy of Medical Sciences and Peking Union Medical College, China（中国医学科学院与北京协和医学院）；School of Computer Science, Wuhan University, China（武汉大学计算机学院）；Innovation Centre for Information, Binjiang Institute of Zhejiang University（浙江大学滨江学院信息创新中心）；Department of Data and Information, Children's Hospital, Zhejiang University School of Medicine（浙江大学医学院附属儿童医院数据与信息部）
National Tsing Hua University（清华大学）；University of Notre Dame（圣母大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Acadamic of Medical Science（广东医学科学院）
Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patients' characteristics, surrounding environment, etc. However, existing deep-learning-based artifact reduction methods are restricted by their training set with specific predetermined artifact types and patterns. As such, they have limited clinical adoption. In this paper, we introduce One-Shot medical image Artifact Reduction (OSAR), which exploits the power of deep learning but without using pre-trained general networks. Specific…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Eindhoven University of Technology（埃因霍温理工大学）
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. For the LIDC-IDRI dataset, we show that u…
REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
Department of Computer Science, Northwestern University McCormick School of Engineering and Applied Science（西北大学麦考密克工程与应用科学学院计算机科学系）；Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine（西北大学范伯格医学院肺与危重症医学科）；Division of Rheumatology, Northwestern University Feinberg School of Medicine（西北大学范伯格医学院风湿病科）；Simpson Querrey Lung Institute for Translational Science, Northwestern University Feinberg School of Medicine（西北大学范伯格医学院辛普森·奎雷转化科学肺研究所）；Department of Electrical and Computer Engineering, Northwestern University McCormick School of Engineering and Applied Science（西北大学麦考密克工程与应用科学学院电气与计算机工程系）；Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University Feinberg School of Medicine（西北大学范伯格医学院放射科机器与混合智能实验室）
