Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for 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）；Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Shanghai Jiaotong University（上海交通大学）；the Chinese University of Hong Kong（香港中文大学）
Segment Anything Model (SAM) 的当前变体（包括原始 SAM 和 Medical SAM）仍然缺乏为医学图像生成足够准确分割的能力。在医学成像场景中，人类专家在 SAM 生成其分割预测后，对特定测试样本的分割进行修正并不罕见。这些修正通常需要使用最先进的标注工具进行手动或半手动校正。受此过程启发，我们引入了一种新方法，利用在线机器学习的优势在测试阶段增强 Segment Anything (SA)。我们使用修正后的标注进行在线学习，旨在提高 SA 在医学图像上的分割质量。为了提高与 SAM 等大规模视觉模型集成时在线学习的有效性和效率，我们提出了一种名为 Auxiliary Online Learning (AuxOL) 的新方法。AuxOL 创建并应用一个小型辅助模型（专家）与 SAM（通才）协同工作，包含自适应在线-批处理和自适应分割融合。在涵盖四种医学成像模态的八个数据集上进行的实验验证了所提方法的有效性。我们的工作提出并验证了一种新的、实用的、有效的方法，用于增强 SA 在下游分割任务（例如医学图像分割）上的表现。
英文摘要：
The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical images. In medical imaging contexts, it is not uncommon for human experts to rectify segmentations of specific test samples after SAM generates its segmentation predictions. These rectifications typically entail manual or semi-manual corrections employing state-of-the-art annotation tools. Motivated by this process, we introduce a novel approach that leverages the advantages of online machine learning to enhance Segment Anything (SA) during test time. We employ rectified annotations to perform online learning, with the aim of improving the segmentation quality of SA on medical images. To improve the effectiveness and efficiency of online learning when integrated with large-scale vision models like SAM, we propose a new method called Auxiliary Online Learning (AuxOL). AuxOL creates and applies a small auxiliary model (specialist) in conjunction with SAM (generalist), entails adaptive online-batch and adaptive segmentation fusion. Experiments conducted on eight datasets covering four medical imaging modalities validate the effectiveness of the proposed method. Our work proposes and validates a new, practical, and effective approach for enhancing SA on downstream segmentation tasks (e.g., medical image segmentation).
Modality-Agnostic Prompt Learning for Multi-Modal Camouflaged Object Detection
Information Science and Technology College, Dalian Maritime University（大连海事大学信息科学与技术学院）；Beijing University of Technology（北京理工大学）；Dalian University of Technology（大连理工大学）
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, 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、法国斯特拉斯堡）；Department of General Surgery, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan（高雄长庚纪念医院外科部、长庚大学医学院）
Segment Anything Model is a Good Teacher for Local Feature Learning
The University of Hong Kong（香港大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Northwestern University（西北大学）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（计算能力网络与信息安全重点实验室，教育部长江计算机科学中心（济南国家超算中心），齐鲁工业大学（山东科学院））；Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science（山东省计算机网络重点实验室，山东省计算机科学基础研究中心）
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）
RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation
College of Information Science and Technology, Nanjing Forestry University（南京林业大学信息科学技术学院）；Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics（南京航空航天大学深圳研究院）
E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation
the National-Reginoal Key Technology Engineering Laboratory for Medical Ultrasound（医学超声关键技术工程实验室）；Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；School of Biomedical Engineering（生物医学工程学院）；Shenzhen University Medical School（深圳大学医学院）；Shenzhen University（深圳大学）；the Institute of Maternal and Child Medicine（妇幼医学研究院）；Shenzhen Maternity and Child Healthcare Hospital（深圳妇幼保健院）；Southern Medical University（南方医科大学）；Medical AI Lab（医学人工智能实验室）；the Department of Biomedical Engineering（生物医学工程系）；the Department of Computer and Data Science（计算机与数据科学系）；Case Western Reserve University（凯斯西储大学）
SARTM: Segment Any RGB Thermal Model with Language aided Distillation
Changchun Institute of Optics, Fine Mechanicsand Physics, Chinese Academy of Sciences University（长春光学精密机械与物理研究所，中国科学院大学）；School of Computing, Macquarie University（麦考瑞大学计算机学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）
Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
The University of Sydney（悉尼大学）；Harvard Medical School（哈佛医学院）；University of Electronic Science and Technology of China（电子科技大学）；Brigham and Women’s Hospital（布莱根妇女医院）
针对无监督医学图像配准缺乏解剖知识的问题，提出融合 SAM 分割先验、原型学习与轮廓感知损失的配准框架，在多数据集上显著提升复杂结构和模糊边界场景的配准性能。
