Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in real-world applications: 1) only limited labels are available for model training, due to expensive annotation costs over medical images; 2) labeled images may contain considerable label noise (e.g., mislabeling labels) due to diagnostic difficulties of diseases. To address these, we seek to exploit rich labeled data from relevant domains to help the learning in the target task via {Unsupervised Domain Adaptation} (UDA). Unlike most UDA methods that rely on clean labeled data or assume samples are equally transferable, we innovatively propose a Collaborative Unsupervised Domain Adaptation algorithm, which conducts transferability-aware adaptation and conquers label noise in a collaborative way. We theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images. Promising experimental results demonstrate the superiority and generalization of the proposed method.
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
Tyris Tech S.L.（泰瑞斯科技公司）；Universitat Politècnica de València（瓦伦西亚理工大学）；ValgrAI – Valencian Graduate School and Research Network for Artificial Intelligence（瓦伦西亚人工智能研究生院与研究网络）
Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
School of Software Yunnan University（软件学院 云南大学）；Department of Computer Science The University of Sheffield（计算机科学系 剑桥大学）；Department of Computing Xian JiaoTong-Liverpool University（计算系 西交利物浦大学）
Unsupervised Machine Learning for Osteoporosis Diagnosis Using Singh Index Clustering on Hip Radiographs
Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology（计算机技术系，计算学院，SRM科学与技术学院）；Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia（通信技术与网络系，计算机科学与信息技术学院，马来西亚国立大学）；Department of Orthopaedics, Sri Ramachandra Medical College（骨科系，Sri Ramachandra医学院）
Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence
Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院附属邵逸夫医院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Key Laboratory for Corneal Diseases Research of Zhejiang Province（浙江省角膜病研究重点实验室）；School of Software Technology, Zhejiang University（浙江大学软件学院）
Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database
University of Rijeka – Faculty of Engineering（里耶卡大学工程学院）；Center for Artificial Intelligence and Cybersecurity（人工智能与网络安全中心）；Medical University of Graz（格拉茨医科大学）
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）
Research Center for Medical Big Data, National Institute of Informatics（日本情报学研究所医疗大数据研究中心）；LPixel Inc.（LPixel公司）；Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）；Department of Radiology, University of Cambridge（剑桥大学放射科）；Cancer Research UK Cambridge Centre（英国癌症研究中心剑桥中心）；Department of Artificial Intelligence, ELTE Eötvös Loránd University（厄特沃什罗兰大学人工智能系）；Department of Rehabilitation Medicine, Juntendo University School of Medicine（顺天堂大学医学部康复医学科）；International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo（东京大学国际神经智能研究中心（WPI-IRCN））
Unsupervised Causal Prototypical Networks for De-biased Interpretable Dermoscopy Diagnosis
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems（浙江可及感知与智能系统重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；Hangzhou Dianzi University（杭州电子科技大学）；Xi'an Jiaotong University（西安交通大学）
AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis
The Chinese University of Hong Kong（香港中文大学）；Central South University（中南大学）；Huawei Technologies Co., Ltd（华为技术有限公司）；The Chinese University of Hong Kong, Shenzhen（香港中文大学深圳校区）
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
University of Florida（佛罗里达大学）；UNC Charlotte（北卡罗来纳大学夏洛特分校）；Xi’an Jiaotong University（西安交通大学）；Sensetime Research（商汤科技研究院）
最近，深度神经网络在注释良好的数据集中表现出与董事会认证的眼科医生相当甚至更好的性能。然而，视网膜成像设备的多样性带来了一个重大挑战：域转移，这导致在将深度学习模型应用于新的测试域时性能下降。在本文中，我们提出了一种新的无监督域自适应框架，称为协作特征集成自适应（CFEA），以有效地克服这一挑战。我们提出的CFEA是一个交互式的范例，通过对抗学习和集成权重呈现出一种精致的协作适应。特别是，我们同时实现了域不变性，并保持了历史预测的指数移动平均值，这通过在训练期间集成权重来实现对未标记数据的更好预测。在不注释来自目标域的任何样本的情况下，编码器和解码器层中的多个对抗性损失引导域不变特征的提取，以混淆域分类器，同时有利于平滑权重的集成。综合实验结果表明，我们的CFEA模型可以克服性能下降，并优于国家的最先进的方法在分割视网膜视盘和杯眼底图像。\textit{Code is available at \url{https://github.com/cswin/AWC}}。
Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor
University of Pennsylvania（宾夕法尼亚大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；The Affiliated Shenzhen School of Guangdong Experimental High School（广东实验中学附属深圳学校）；Pazhou Lab（琶洲实验室）
ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator
1 Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China 2 Amazon, USA 3 Shanghai Artificial Intelligence Laboratory, China 4 Department of Geriatrics, Third Affiliated Hospital of Sun Yat-sen University, China 5 Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
针对 AD 分析中多模态、任意模态组合及缺失输入的任务需求，论文提出基于 LLM 的专用智能体 ADAgent，通过推理引擎、医学工具与协作结果协调器提升诊断和预后性能。
Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing
College of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）；Industrial Training Center, Guangdong Polytechnic Normal University（广东技术师范大学工业训练中心）
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Nankai University（南开大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Southern California（南加州大学）；China University of Mining and Technology（中国矿业大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学协同媒体网络创新中心）；Microsoft Research Asia（微软亚洲研究院）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）
Unsupervised Collaborative Domain Adaptation for Driving Scene Parsing
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息学院）；Department of Control Science and Engineering, Harbin Institute of Technology（控制科学与工程系，哈尔滨工业大学）；Department of Vehicle Control System and Software Development, NIO（车辆控制系统与软件开发部，蔚来汽车）；School of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）；Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University（嵌入式系统与服务计算重点实验室（教育部），同济大学）
SkinGPT-X: A Self-Evolving Collaborative Multi-Agent System for Transparent and Trustworthy Dermatological Diagnosis
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Department of Dermatology, Tianjin Institute of Integrative Dermatology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital（天津中医药大学附属医院天津市中西医结合皮肤病研究所皮肤科）；Department of Dermatology, Beijing AnZhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院皮肤科）
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography
Samsung Medical Center（三星医疗中心）；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Sungkyunkwan University（成均馆大学）
ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis
Faculty of Applied Sciences, Macao Polytechnic University, Macau, China（澳门理工大学应用科学学院）；Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China（深圳大学生物医学工程学院）；Netherlands Cancer Institute, Amsterdam, Netherlands（荷兰癌症研究所）；Department of Ultrasound, Peking University Third Hospital, Beijing, China（北京第三人民医院超声科）
