Editorial: Introduction to the Issue on Domain Enriched Learning for Medical Imaging

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The nineteen papers in this special section focus on domain enriched learning for medical imaging. In recent years, learning based methods have emerged to complement traditional model and feature based methods for a variety of medical imaging problems such as image formation, classification and segmentation, quality enhancement etc. In the case of deep neural networks, many solutions have achieved unprecedented performance gains and have defined a new state of the art. Despite the progress, compelling open challenges remain. One such key challenge is that many learning frameworks (notably deep learning) are purely data-driven approaches and their performance depends strongly on the quantity and quality of training image data available. When training is limited or noisy, the performance drops sharply. Deep neural networks based approaches additionally face the challenge of often not being straightforward to interpret. Fortunately, exciting recent progress has emerged in enriching learning frameworks with domain knowledge and signal structure. As a couple of representative examples: in image reconstruction problems, this may involve using statistical/structural image priors; for image segmentation, shape and anatomical knowledge (conveyed by an expert) may be leveraged, etc. This special issue brings together contributions that combine signal, image priors and other flavors of domain knowledge with machine learning methods for solving many diverse medical imaging problems.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-10
Language
English
Article Type
Editorial Material
Citation

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, v.14, no.6, pp.1068 - 1071

ISSN
1932-4553
DOI
10.1109/JSTSP.2020.3021275
URI
http://hdl.handle.net/10203/277027
Appears in Collection
AI-Journal Papers(저널논문)
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