A Survey on Deep Learning in Medical Image Analysis
It provides a comprehensive survey for researchers in medical imaging, but it is incremental as it summarizes existing work without new methods.
This paper reviews deep learning concepts and over 300 recent contributions in medical image analysis, covering tasks like classification and segmentation, and discusses open challenges.
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 and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.