Deep Learning in Medical Image Analysis

Over the last decades, we have witnessed the importance of medical imaging, e.g., computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET), mammography, ultrasound, X-ray, and so on, for the early detection, diagnosis, and treatment of diseases (1). In the clinic, the medical image interpretation has mostly been performed by human experts such as radiologists and physicians. However, due to large variations in pathology and potential fatigue of human experts, researchers and doctors have recently begun to benefit from computer-assisted interventions. While, compared to the advances in medical imaging technologies, it is belated for the advances in computational medical image analysis, it has recently been improving with the help of machine learning techniques. In the stream of applying machine learning for data analysis, meaningful feature extraction or feature representation lies at the heart of its success to accomplish target tasks.