Over the past years, deep learning has established itself as a powerful tool across a broad spectrum of domains in imaging, e.g., classification, prediction, detection, segmentation, diagnosis, interpretation, reconstruction, etc. While deep neural networks initially found nurture in the computer vision community, they have quickly spread over medical imaging applications.
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed-up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data is crucially important for clinical applications and in understanding the underlying biological process.
The purpose of this Special Issue “Deep Learning on Medical Image Analysis” is to present and highlight novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.