Vision foundation models like the Segment Anything Model (SAM), pretrained on large-scale natural image datasets, often struggle in medical image segmentation due to a lack of domain-specific adaptation. In clinical practice, fine-tuning such models efficiently for medical downstream tasks with minimal resource demands, while maintaining strong performance, is challenging. To address these issues, we propose BALR-SAM, a boundary-aware low-rank adaptation framework that enhances SAM for medical imaging. It combines three tailored components: (1) a Complementary Detail Enhancement Network (CDEN) using depthwise separable convolutions and multi-scale fusion to capture boundary-sensitive features essential for accurate segmentation; (2) low-rank adapters integrated into SAM's Vision Transformer blocks to optimize feature representation and attention for medical contexts, while simultaneously significantly reducing the parameter space; and (3) a low-rank tensor attention mechanism in the mask decoder, cutting memory usage by 75% and boosting inference speed. Experiments on standard medical segmentation datasets show that BALR-SAM, without requiring prompts, outperforms several state-of-the-art (SOTA) methods, including fully fine-tuned MedSAM, while updating just 1.8% (11.7M) of its parameters.
Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation
University of Central Florida, Orlando, USA（佛罗里达大学）；University College London, London, UK（伦敦大学学院）；University of Illinois Urbana-Champaign, Champaign, USA（伊利诺伊大学厄巴纳-香槟分校）；University of Trento, Trento, Italy（特伦托大学）；The Hong Kong Polytechnic University, China（香港理工大学）；University of Pennsylvania, Philadelphia, USA（宾夕法尼亚大学）
Medical SAM3通过微调SAM3模型，提升医学图像分割的通用提示驱动能力，解决领域偏移和复杂结构推理问题。
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；University of Alberta（阿尔伯塔大学）；A*STAR（新加坡科技研究局）；Carnegie Mellon University（卡内基梅隆大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Hongkong（香港大学）
本文提出Medical SAM Adapter（Med-SA），通过轻量适配技术将医学知识融入SAM，利用SD-Trans和HyP-Adpt适配2D模型至3D医学图像，在17个任务上以仅2%参数更新超越SOTA方法。
SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation
School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息工程学院）；Pazhou Lab（琶洲实验室）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentation
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；University of Liverpool（利物浦大学）；Monash University（莫纳什大学）；Shanghai Jiao Tong University（上海交通大学）
SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation
Institute of Information Technology (ITEC), University of Klagenfurt, Austria（信息科技研究所（ITEC），克莱芬福特大学，奥地利）；Center for Artificial Intelligence in Medicine, University of Bern, Switzerland（医学人工智能中心，伯恩大学，瑞士）；Department of Biomedical Engineering, Eindhoven University of Technology, Netherlands（生物医学工程系，埃因霍温理工大学，荷兰）
Interactive 3D Medical Image Segmentation with SAM 2
East China Normal University（华东师范大学）；Tongji University（同济大学）；Shanghai United Imaging Healthcare Advanced Technology Research Institute Co., Ltd.（上海联影医疗科技先进技术研究院有限公司）
本文提出将 3D 医学切片视为视频帧、利用 SAM 2 零样本传播单帧标注的交互式分割流程，实验表明其可在特定场景缩小与监督方法差距并降低标注成本。
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
ML-Labs, Dublin City University（都柏林城市大学 ML-Labs）；ML-Labs, University College Dublin（都柏林大学 ML-Labs）；Centre for Applied AI (CeADAR), University College Dublin（都柏林大学应用人工智能中心 (CeADAR)）；Insight SFI Centre for Data Analytics, Dublin City University（都柏林城市大学 Insight SFI 数据分析中心）
Johns Hopkins University（约翰斯·霍普金斯大学）；King’s College London（伦敦国王学院）；Tsinghua University（清华大学）
The Segment Anything Model (SAM) has drawn significant attention from researchers who work on medical image segmentation because of its generalizability. However, researchers have found that SAM may have limited performance on medical images compared to state-of-the-art non-foundation models. Regardless, the community sees potential in extending, fine-tuning, modifying, and evaluating SAM for analysis of medical imaging. An increasing number of works have been published focusing on the mentioned four directions, where variants of SAM are proposed. To this end, a unified platform helps push the…
Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation
East China Normal University（华东师范大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Shanghai Jiao Tong University（上海交通大学）
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）
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）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Biomedical Engineering, University of Science and Technology of China（中国科学技术大学生物医学工程学院）
针对 SAM 难以建模三维医学切片上下文且全体积提示成本高的问题，本文提出 Slide-SAM，利用三切片滑动窗口和相邻掩码传播提示，实现极少提示下的高效三维分割。
DB-SAM: Delving into High Quality Universal Medical Image Segmentation
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Tianjin University（天津大学）；Institute of High Performance Computing（高性能计算研究所）；Agency for Science, Technology and Research（科学、技术与研究局）；Linköping University（林雪平大学）
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）
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）
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）
South China University of Technology（华南理工大学）；University of Utah（犹他大学）
We introduce the idea of inter-slice image augmentation whereby the numbers of the medical images and the corresponding segmentation labels are increased between two consecutive images in order to boost medical image segmentation accuracy. Unlike conventional data augmentation methods in medical imaging, which only increase the number of training samples directly by adding new virtual samples using simple parameterized transformations such as rotation, flipping, scaling, etc., we aim to augment data based on the relationship between two consecutive images, which increases not only the number b…
Vector Institute（矢量研究所）；University of Toronto（多伦多大学）；CIFAR（加拿大高等研究院）
This work explores the use of computer vision for image segmentation and classification of medical fluid samples in transparent containers (for example, tubes, syringes, infusion bags). Handling fluids such as infusion fluids, blood, and urine samples is a significant part of the work carried out in medical labs and hospitals. The ability to accurately identify and segment the liquids and the vessels that contain them from images can help in automating such processes. Modern computer vision typically involves training deep neural nets on large datasets of annotated images. This work presents a…
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））
Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属新华医院超声科）
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
KOKONI, Moxin (Huzhou) Tech. Co., LTD（莫西（湖州）科技有限公司KOKONI）；Beihang University（北京航空航天大学）；Zhejiang University（浙江大学）；Huzhou University（湖州师范学院）；Singapore University of Technology and Design（新加坡科技设计大学）
DEAP-3DSAM: Decoder Enhanced and Auto Prompt SAM for 3D Medical Image Segmentation
College of Computer Science and Tech.（计算机科学与技术学院）；National University of Defense Tech.（国防科技大学）；Department of GI Medical Onco.（胃肠医学肿瘤科）；TMUCIH, NCRCC（TMUCIH、NCRCC）
