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A Survey on Deep Learning in Medical Image Analysis - 2017

A Survey On Deep Learning In Medical Image Analysis

Research Area:  Machine Learning

Abstract:

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

Keywords:  

Author(s) Name:  Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. S├ínchez

Journal name:  Medical Image Analysis

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.media.2017.07.005

Volume Information:  Volume 42, December 2017, Pages 60-88