Trustworthy Artificial Intelligence for Medical Imaging
Artificial intelligence (AI) has achieved or even exceeded human performance in many medical imaging tasks, owing to the fast development of AI techniques and the growing scale of medical data. However, AI techniques are still far from being widely applied in medical imaging practice. Real-world scenarios are far more complex, and AI is often faced with challenges in its credibility such as lack of explainability, generalization, fairness, privacy, etc. The development of trustworthy artificial intelligence for medical imaging is hence of great importance to enhance the trust and confidence of doctors and patients in using the related techniques. We aim to bring together researchers to provide different perspectives on how to develop trustworthy AI algorithms to accelerate the landing of AI in medical imaging.
Guest editors:
Prof. Hao Chen
The Hong Kong University of Science and Technology, Hong Kong, Hong Kong
(Trustworthy AI, medical image analysis, deep learning, computer vision, bioinformatics)
Prof. Yuyin Zhou
University of California, Santa Cruz, California, United States of America
(AI for healthcare, medical image computing, computer vision, machine learning)
Dr. Luyang Luo
The Hong Kong University of Science and Technology, Hong Kong, Hong Kong
(Trustworthy AI, label-efficient learning, multimodal learning, biomedical data analysis, foundation model)
Prof. Lequan Yu
University of Hong Kong, Hong Kong, Hong Kong
(Machine learning, biomedical data analysis, multimodal learning, real-world learning, causality-driven learning)
Dr. Junlin Hou
Ramakrishna Mission Vidyamandira, Howrah, India
(Deep learning, medical image analysis, explainable AI, label-efficient learning)
Dr. Xin Wang
The Chinese University of Hong Kong, Hong Kong, Hong Kong
(Weakly supervised learning, semi-supervised learning, medical image analysis, AI for drug discovery, precision oncology )
Manuscript submission information:
Manuscript submission deadline: 31/12/2024
You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Prof. Hao Chen via jhc@cse.ust.hk.
Please refer to the https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics/publish/guide-for-authors to prepare your manuscript, and select the article type of “VSI: TAI4MI” when submitting your manuscript online at the journal’s submission platform https://www.editorialmanager.com/cmig/default.aspx . Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.
Keywords:
(Generalization) AND (Explainability) AND (Reasoning) AND (Debias) AND (Fairness) AND (Uncertainty) AND (Privacy-preserving AI) AND (Human-AI cooperation) AND (Multi-modal) AND (Foundation model)
Computerized Medical Imaging and Graphics的CAR指数
2023年3月份科睿唯安官方一次性踢除35本SCI期刊，多数涉及学术诚信问题，让我们意识到学术期刊的“被踢”指数，也很重要。目前，对于期刊的“被踢”指数，这里介绍一下：CAR指数（关于CAR的详细介绍，请关注：www.jcarindex.com），这是一种评价期刊学术诚信风险的指数，指数越高代表可能的风险越大。从数据看，Computerized Medical Imaging and Graphics不管是2022年度，还是2023年度的CAR指数，都是比较低的。当然，CAR指数仅供参考，期刊风险情况，需以科睿唯安或中科院预警等官方为准！
让推送更美好~
