Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and demonstrated promising generalization ability. In this work, we propose ViP, a novel visual symptom-guided prompt learning framework for medical image analysis, which facilitates general knowledge transfer from CLIP. ViP consists of two key components: a visual symptom generator (VSG) and a dual-prompt network. Specifically, VSG aims to extract explicable visual symptoms from pre-trained large language models, while the dual-prompt network utilizes these visual symptoms to guide the training on two learnable prompt modules, i.e., context prompt and merge prompt, which effectively adapts our framework to medical image analysis via large VLMs. Extensive experimental results demonstrate that ViP can outperform state-of-the-art methods on two challenging datasets.
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images
The Hong Kong University of Sciences and Technology, Guangzhou（香港科学与技术大学（广州））；Beihang University（北航大学）；Shandong University（山东大学）；Hubei University（湖北大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；Australian National University（澳大利亚国立大学）
Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications
Sun Yat-sen University（中山大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong University（西安交通大学）；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院）；University of Portsmouth（朴茨茅斯大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Qingdao Academy of Intelligent Industries（青岛智能产业技术研究院）
Aligning Fetal Anatomy with Kinematic Tree Log-Euclidean PolyRigid Transforms
Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA（麻省理工学院计算机科学与人工智能实验室）；California Institute of Technology, Pasadena, USA（加州理工学院）；Boston Children’s Hospital and Harvard Medical School, Boston, USA（哈佛医学院波士顿儿童医院）；The Hebrew University of Jerusalem, Jerusalem, Israel（耶路撒冷希伯来大学）
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification
University of Pittsburgh（匹兹堡大学）；National University of Defense Technology（国防科技大学）；University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh（匹兹堡大学医学中心匹兹堡儿童医院）；Magee-Womens Hospital of University of Pittsburgh Medical Center（匹兹堡大学医学中心马吉妇女医院）
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Big Data and Brain Computing（北京大数据与脑计算高精尖创新中心）；Hangzhou Innovation Institute of Beihang University（北京航空航天大学杭州创新研究院）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Technology Sydney（悉尼科技大学）
Grounded Knowledge-Enhanced Medical Vision-Language Pre-training for Chest X-Ray
The Chinese University of Hong Kong（香港中文大学）；CU Lab for AI in Radiology (CLAIR), The Chinese University of Hong Kong（香港中文大学放射学人工智能实验室（CLAIR））；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；China Unicom Global Limited（中国联通国际有限公司）
Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI
National Center for Tumor Diseases (NCT)（肿瘤疾病国家中心）；German Cancer Research Center (DKFZ) Heidelberg（德国癌症研究中心（Heidelberg））；Faculty of Mathematics and Computer Science（数学和计算机科学学院）；Heidelberg University（海德堡大学）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health - 健康信息与数据科学学校）；Medical Faculty（医学学院）；Pattern Analysis and Learning Group（模式分析与学习小组）
Aligning What You Separate: Denoised Patch Mixing for Source-Free Domain Adaptation in Medical Image Segmentation
1 Carnegie Mellon University, PA, USA 2 PASSIO Lab, North Carolina A\&T State University, NC, USA 3 Ho Chi Minh University of Science, Vietnam 4 Ho Chi Minh University of Technology, Vietnam 5 Northwestern University, IL, USA
HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models
Zhejiang University（浙江大学）；Byte Dance（字节跳动）；National University of Singapore（新加坡国立大学）；Angelalign Inc China（Angelalign中国公司）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）
Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning
Tongji University（同济大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（华东师范大学）；Stanford University（斯坦福大学）；New York University（纽约大学）
本文提出 VaLiK 方法，通过跨模态信息补充构建无需注释的多模态知识图谱，提升大语言模型推理能力。
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
The Hong Kong Polytechnic University（香港理工大学）；West China Hospital of Sichuan University（四川大学华西医院）；Sichuan Academy of Medical Sciences（四川省医学科学院）；Sichuan Provincial People's Hospital（四川省人民医院）
