What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

Artificial Intelligence (AI) is having a tremendous impact across most areas of science.
Applications of AI in healthcare have the potential to improve our ability to detect, diagnose …

Background and ObjectiveProcessing of medical images such as MRI or CT presents
different challenges compared to RGB images typically used in computer vision. These …

Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel
level, most benchmark datasets are equipped with the annotations of only one type of …

Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …

Accurate multi-organ abdominal CT segmentation is essential to many clinical applications
such as computer-aided intervention. As data annotation requires massive human labor …

Early detection and diagnosis of a brain tumor enhance the medical options and the
patient's chance of recovery. Magnetic resonance imaging (MRI) is used to detect and …

Deep learning has made significant achievements in many application areas. To train and
test models more efficiently, enterprise developers submit and run their deep learning …

In this paper, we introduce and compare different approaches for incorporating shape prior
information into neural network-based image segmentation. Specifically, we introduce the …