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As neuroimaging scanners grow in popularity in hospitals and institutes, the tasks of radiologists are increasing. Emotion, fatigue, and other factors may influence the manual interpretation of results. This manual interpretation suffers from inter- and intra-radiologist variance. Computer-aided medical diagnosis (CAMD) are procedures in medicine that assist radiologists and doctors in the interpretation of medical images, which may come from CT, X-ray, ultrasound, thermography, MRI, PET, SPECT, etc. In practice, CAMD can help radiologists to interpret medical images within seconds. Conventional CAMD tools are built on top of handcrafted features. Recent progress on deep learning opens a new era in which features can be automatically built from a large amount of data. Many important medical projects were launched during the last decade (Human brain project, Blue brain project, Brain Initiative, etc.) that provide massive amounts of data. This emerging big medical data can support the use of deep learning.

IEEE Access Special Section Editorial: Deep Learning for Computer-Aided Medical Diagnosis