Machine learning methods have been successfully applied to many applications. One such application that has received considerable attention in recent years is medical imaging. With improvements in computer hardware and the availability of enormous amounts of data, machine learning approaches, such as deep learning, simplify feature engineering and have shown great promise in medical image analysis. However, there are still many remaining challenges, for example, inconsistency in data format and lack of labeled training data that need to be addressed.

The aim of this special issue is to highlight state-of-the-art research on medical imaging with machine learning methods. High-quality research articles and reviews are welcome. The latest progress on medical image processing with deep learning is of special interest.

Potential topics include but are not limited to the following:

• Machine Learning for Medical image analysis
• Multitask learning for Medical image reconstruction
• Fuzzy learning method in Disease identification/diagnosis
• Multimodal Machine learning for data fusion in medical imaging
• Ensemble learning for Real-time health monitoring systems
• Supervised, unsupervised, and semisupervised learning for medical imaging
• Deep learning for medical image processing