Deep Learning in Medical Image Analysis

Dinggang Shen, Guorong Wu, Heung-Il Suk
2017 Annual Review of Biomedical Engineering  
The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge,
more » ... at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements. Keywords Medical image analysis; deep learning; unsupervised feature learning D. Shen and H.-I. Suk are the co-corresponding authors DISCLOSURE STATEMENT The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.
doi:10.1146/annurev-bioeng-071516-044442 pmid:28301734 pmcid:PMC5479722 fatcat:amn6qgpt6fedzp3zejgi4aw66u