In recent years, the rapid rise of biological data has necessitated the development of data-driven computational algorithms for the analysis of large-scale biological data in a time-efficient and accurate fashion. The Healthcare 4.0 paradigm, at its core, involves providing highly personalized services to patients. For this to be realized in the healthcare industry, real-time service is of the essence. Medical and biological technology, in particular, have provided us with massive amounts of biological and physiological data, including medical text, signals, images, genomic and protein sequences, and other types of biological data. Discovering more about human health and disease as well as the best techniques for treatment will be made easier as a result of this data.

Computational techniques, including machine/deep learning approaches, as well as other 'intelligent' technologies, have lately emerged in academia and industry as 'intelligent' tools for gaining insight from medical and biological data in a wide range of specific healthcare sectors. Machine/deep learning usually deeps on features used in the learning process. The goal of making learning algorithms less dependent on handmade feature engineering is to broaden the scope and ease with which they can be used. This will allow for the development of fresh applications.

This Special Issue will focus on cutting-edge computational (machine/deep learning) approaches in medical image analysis which is undeniably a vital task for radiologists and medical physicists. This allows specialists to measure and monitor the treatment's efficiency and patient reaction. A wide range of original research articles and reviews on computational and intelligent approaches in medical image analysis and their applications (e.g., breast cancer, lung cancer, brain tumor, etc.) will be considered for publication in this Special Issue. We would like to collect relevant papers that introduce novel tools for the analysis of medical images that are driven by computational methods, such as machine/deep learning. Research describing multidisciplinary approaches or applications is especially encouraged. We also invite authors to make their programs and experimental data available to the public so that our Special Issue can be more infusive and visually appealing.

Potential topics include but are not limited to the following:

• Active machine learning for medical image analysis
• Deep learning for medical image analysis
• Multitask machine learning for medical image analysis
• Supervised learning algorithms for medical image analysis
• Unsupervised learning algorithms for medical image analysis
• Imbalanced learning algorithms for medical image analysis
• Multiview feature learning for medical image analysis
• Deep learning-based feature learning strategies for medical image analysis
• Feature representation optimization algorithms for medical image analysis
• Computational and mathematical techniques for medical image analysis
• Security and privacy of medical image-based systems