A survey on deep learning in medical image analysis.
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 Sánchez
Author Information
Geert Litjens: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: geert.litjens@radboudumc.nl.
Thijs Kooi: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Babak Ehteshami Bejnordi: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Arnaud Arindra Adiyoso Setio: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Francesco Ciompi: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Mohsen Ghafoorian: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Jeroen A W M van der Laak: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Bram van Ginneken: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Clara I Sánchez: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
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.
