Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
Northwestern University（西北大学）；Ege University（爱琴海大学）；Tokat State Hospital（托卡特国立医院）；NVIDIA（英伟达）；University of Catania（卡塔尼亚大学）；Columbia University Medical Center（哥伦比亚大学医学中心）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）
Ugur Demir, Ismail Irmakci, Elif Keles, Ahmet Topcu, Ziyue Xu, Concetto Spampinato, Sachin Jambawalikar, Evrim Turkbey, Baris Turkbey, Ulas Bagci
Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications. We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels. This approach overcomes the drawbacks of commonly used Grad-CAM and its extended versions. The premise behind our proposed strategy is that the information flow is minimized while ensuring the classifier prediction stays similar. Our findings indicate that the bottleneck condition provides a more stable severity estimation than the similar attribution methods.
Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications
Concordia Institute for Information Systems Engineering (CIISE)（康考迪亚信息系统工程研究所）；Concordia University（康考迪亚大学）；Sunnybrook Health Sciences Centre（森尼布鲁克健康科学中心）；University of Toronto（多伦多大学）；McGill University Health Center-Research Institute（麦吉尔大学健康中心研究所）；University of Calgary（卡尔加里大学）
A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis
School of Software Engineering, South China University of Technology（华南理工大学软件工程学院）；Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University（中山大学口腔颌面外科医院）；Mohamed Bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；School of Computer Science, University of Adelaide（阿德莱德大学计算机科学学院）
Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images
Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心健康人工智能研究所）；Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
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。
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai University（上海大学）；United Imaging Intelligence（联影智能）；Shanghai Jiao Tong University（上海交通大学）；Beihang University（北京航空航天大学）；Medical School of Nanjing University（南京大学医学院）；National Institute of Healthcare Data Science at Nanjing University（南京大学国家医疗数据科学研究院）
Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review
Institute of Technology, University Center Salhi Ahmed, Naama, Algeria（阿尔及利亚纳马大学中心沙利·阿赫迈德技术学院）；LSEA Laboratory, Electrical Engineering Department, University of Medea, 26000, Algeria（阿尔及利亚梅德亚大学电子工程系LSEA实验室）；College of Engineering and Information Technology, University of Dubai, Dubai, UAE（阿联酋迪拜大学工程与信息技术学院）；Cybersecurity Institute, Department of Computer Science, University of Liverpool, Liverpool, UK（英国利物浦大学计算机科学系网络安全研究所）
