Guest Editorial Multimodal Learning in Medical Imaging Informatics

The papers in this special section focus on multimodal learning in medical imaging informatics. Enormous amounts of health-related data are produced daily, such as those from personal devices, e.g., fitness trackers or mobile applications, ambient sensors, clinical data in electronic health records, pathology reports, lab results, medical images, voice recordings, etc. The practice of modern medicine increasingly relies on data from multiple sources to guide better care. Toward this goal, the papers in this section describe the tools and techniques that integrate multiple data types to describe a particular medical event/case toward developing higher confidence in their decision-making and guidance.