From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation
Technical University of Munich（慕尼黑技术大学）；Korea University（韩国大学）；Kyung Hee University（庆熙大学）；Seoul National University of Science and Technology（首尔科学技术大学）
Evren Çetinkaya, Sangmin Lee, Jung Uk Kim, Hong Joo Lee, Nassir Navab
Visual prompting has emerged as a powerful method for adapting pre-trained models to new domains without updating model parameters. However, existing prompting methods typically optimize a single prompt per domain and apply it uniformly to all inputs, limiting their ability to generalize under intra and inter-domain variability, which is especially critical in the medical field. To address this, we propose APEX, an Adaptive Prompt EXtraction framework that retrieves input-specific prompts from a learnable prompt memory. The memory stores diverse, domain-discriminative prompt representations and is queried via domain features extracted from the Fourier spectrum. To learn robust and discriminative domain features, we introduce a novel Low-Frequency Feature Contrastive (LFC) learning framework that clusters representations from the same domain while separating those from different domains. Extensive experiments on two medical segmentation tasks demonstrate that APEX significantly improves generalization across both seen and unseen domains. Furthermore, it complements any existing backbones and consistently enhances performance, confirming its effectiveness as a plug-and-play prompting solution in medical fields. The code is available at https://github.com/cetinkayaevren/apex/
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）
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework
State Key Laboratory of Multimodal Artificial Intelligence System, Institute of Automation, Chinese Academy of Science, China（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院，中国）；School of Artificial Intelligence, University of Chinese Academy of Science, China（人工智能学院，中国科学院大学，中国）；Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing（放射科，北医三院，北京医科大学，中国医学科学院，北京）
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；School of Information Science and Technology, Guangdong University of Foreign Studies（广东外语外贸大学信息科学与技术学院）；Faculty of Science and Technology, University of Macau（澳门大学科技学院）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；South China Hospital, Shenzhen University（深圳大学华南医院）
Foundation Model-guided Iteratively Prompting and Pseudo-Labeling for Partially Labeled Medical Image Segmentation
Department of Biomedical Engineering, Columbia University（哥伦比亚大学生物医学工程系）；Department of Radiation Oncology, Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心放射肿瘤科）；Data Science Institute, Columbia University（哥伦比亚大学数据科学研究所）；Herbert Irving Comprehensive Cancer Center, Columbia University（哥伦比亚大学赫伯特·欧文综合癌症中心）；Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院放射肿瘤科）
Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation
The Chinese University of Hong Kong（香港中文大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与XR研究所）
提出 CPC-SAM，在双分支框架中通过 SAM 交叉提示和提示一致性正则化利用标注与未标注数据，显著提升半监督医学图像分割性能。
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
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）；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）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；Medical Artificial Intelligence Research Institute of Renmin Hospital, Wuhan University（武汉大学人民医院医学人工智能研究院）；School of Computer Science, Central China Normal University（华中师范大学计算机学院）
Multi-rater Prompting for Ambiguous Medical Image Segmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Institute of Wenzhou, Zhejiang University（浙江大学温州研究院）；State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院血管植入装置国家重点实验室）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Notre Dame（圣母大学）；School of Public Health Zhejiang University（浙江大学公共卫生学院）
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology Co., Ltd.（影迈医疗科技有限公司）；The Hong Kong Polytechnic University（香港理工大学）；SIAT（深圳先进技术研究院）
This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enha…
Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation
School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计支柱）
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medical field where the data sharing is highly concerned and sometimes prohibitive due to privacy issue. This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which …
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation
School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）
SAM-aware Test-time Adaptation for Universal Medical Image Segmentation
Department of Data Science & AI, Faculty of Information Technology, Monash University（数据科学与人工智能系，信息科技学院，莫纳什大学）；Department of Computing and Department of Brain Sciences, Imperial College London（计算系和脑科学系，伦敦帝国学院）；Mohamed bin Zayed University of Artificial Intelligence（莫卧儿·本·扎耶德人工智能大学）；Department of Radiation Oncology, UT Southwestern Medical Center（放射肿瘤科，德克萨斯西南医学中心）
Concept Alignment Contrast and Long-Short Prompt Memory for Test-Time Adaptation of SAM3 in Medical Image Segmentation
University of Electronic Science and Technology of China（电子科技大学）；Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
East China Normal University（华东师范大学）；Stanford University（斯坦福大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Hong Kong（香港大学）；University of Florida（佛罗里达大学）；UC Santa Cruz（加州大学圣克鲁兹分校）
Carnegie Mellon University（卡内基梅隆大学）；AWS AI Labs（AWS人工智能实验室）
Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts with Residuals (EXPRES) which modifies the prompt learning paradigm specifically for effective adaptation of vision transformers (ViT). Out method constructs downstream representations via learnable ``output'' tokens, that are akin to the learned class tokens of the ViT. Further for better steering of the downstream representation processed by the frozen transformer, we introdu…
Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation
Department of Computer Science, The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校计算机科学系）；Doctoral Training Centre, University of Oxford（牛津大学博士培训中心）；Department of Biomedical Engineering and Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学生物医学工程系和电气与计算机工程系）；Computational Biology Department, Carnegie Mellon University（卡内基梅隆大学计算生物学系）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
University of California Berkeley（加州大学伯克利分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
We propose a segmentation framework that uses deep neural networks and introduce two innovations. First, we describe a biophysics-based domain adaptation method. Second, we propose an automatic method to segment white and gray matter, and cerebrospinal fluid, in addition to tumorous tissue. Regarding our first innovation, we use a domain adaptation framework that combines a novel multispecies biophysical tumor growth model with a generative adversarial model to create realistic looking synthetic multimodal MR images with known segmentation. Regarding our second innovation, we propose an automa…
FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation
Beihang University（北京航空航天大学）；Zhejiang Lab（之江实验室）；Singapore University of Technology and Design（新加坡科技设计大学）；BioMind Technology Center（BioMind技术中心）；Zhongguancun Laboratory（中关村实验室）
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation
Rutgers University（罗格斯大学）；Zhejiang University（浙江大学）；Red Hat AI Innovation（红帽人工智能创新中心）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；NVIDIA Research（英伟达研究院）
SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images
IRCAD France（法国国际消化道疾病研究中心）；Université de Strasbourg, CNRS, ICube（斯特拉斯堡大学、法国国家科学研究中心、ICube实验室）；IRCCS Humanitas Research Hospital（圣心研究医院）；Humanitas University（圣心大学）；Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)（圣心综合大学医院）
ES-VP : Energy-Shaped Dynamic Visual Prompting for Efficient Model Adaptation
Rutgers University（罗格斯大学）；Zhejiang University（浙江大学）；University at Buffalo, SUNY（纽约州立大学布法罗分校）；Adobe Research（奥多比研究院）；University of Connecticut（康涅狄格大学）；Washington University（华盛顿大学）
Kyushu University（九州大学）；Kyoto University（京都大学）；The University of Tokyo（东京大学）
Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a small number of labeled samples from the target domain. In this paper, we develop a semi-supervised domain adaptation method, which has robustness to class-imbalanced situations, which are common in medical image classification tasks. For robustness, we propose a weakly-supervised clustering pipeline to obtain high-purity clusters and utilize the clusters in representation learning for domain adaptation. The proposed …
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
Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；University of British Columbia（不列颠哥伦比亚大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
