Editorial Special Issue on Explainable and Generalizable Deep Learning for Medical Imaging

doi:10.1109/TNNLS.2024.3395937

	Editorial Special Issue on Explainable and Generalizable Deep Learning for Medical Imaging
	Tianming Liu 1; Dajiang Zhu 2; Fei Wang 3; Islem Rekik 4; Xia Hu 5; Dinggang Shen6,7
	2024-06-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (IF:10.2[JCR-2023],10.4[5-Year])
ISSN	2162-2388
EISSN	2162-2388
卷号	35 期号:6 页码:7271-7274
发表状态	已发表
DOI	10.1109/TNNLS.2024.3395937
摘要	The rapid advancements in deep learning technologies have profoundly influenced the field of medical image analysis, yet their full integration into clinical radiology practices has not progressed as quickly as expected. A significant hurdle to their widespread adoption among radiologists and clinicians is the prevailing lack of trust and confidence in the outcomes produced by these technologies. This concern primarily stems from concerns regarding the explainability and generalizability of deep learning models within the realm of medical imaging. As part of the responses from the Medical Image Analysis Community to address these critical issues, we organized the IEEE Transactions on Neural Networks and Learning Systems (TNNLS) Special Issue on explainable and generalizable deep learning for medical imaging. This IEEE TNNLS Special Issue calls for original and innovative methodological contributions that aim to address the key challenges on explainability and generalizability of deep learning for medical imaging. This IEEE TNNLS Special Issue emphasizes the research and advanced development of the technical aspects of new image analysis methodologies, and all the developed new methods should also be evaluated or validated on real and large-scale medical imaging data.
关键词	Deep learning Image analysis Clinical radiology Critical issues Image-analysis Learning models Learning technology Medical image analysis Methodological contributions Neural learning Neural-networks Technical aspects
URL	查看原文
收录类别	EI ; SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001252651500095
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20242416256513
EI主题词	Medical imaging
EI分类号	461.1 Biomedical Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 746 Imaging Techniques
原始文献类型	Journal article (JA)
来源库	IEEE
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/384327
专题	生物医学工程学院生物医学工程学院_PI研究组_沈定刚组
作者单位	1.School of Computing, University of Georgia, Athens, GA, USA 2.Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA 3.Department of Population Health Sciences, Cornell University, Ithaca, NY, USA 4.Imperial College London, London, U.K. 5.Department of Computer Science, Rice University, Houston, TX, USA 6.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China 7.Shanghai United Imaging Intelligence Company Ltd., Shanghai, China
推荐引用方式 GB/T 7714	Tianming Liu,Dajiang Zhu,Fei Wang,et al. Editorial Special Issue on Explainable and Generalizable Deep Learning for Medical Imaging[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2024,35(6):7271-7274.
APA	Tianming Liu,Dajiang Zhu,Fei Wang,Islem Rekik,Xia Hu,&Dinggang Shen.(2024).Editorial Special Issue on Explainable and Generalizable Deep Learning for Medical Imaging.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,35(6),7271-7274.
MLA	Tianming Liu,et al."Editorial Special Issue on Explainable and Generalizable Deep Learning for Medical Imaging".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 35.6(2024):7271-7274.