Journal: Current Medical Imaging
Guest editor(s): Jianqiao Fang

Introduction

Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have a significant and profound impact on analysis and treatment of the human body, therapeutic decisions, and may ultimately improve the outcome for patients. Despite the popular application of these techniques in a wide range of medical image applications, there is still a lack of theoretical and practical understanding of their learning characteristics and decision-making behaviour when applied to medical images. AI tools like machine learning and deep learning can study the inherent complex medical data and extract features to provide necessary outputs. Incorporating advanced AI-based algorithms into the healthcare delivery system has tremendous potential to improve healthcare, specifically in automated diagnosis, assessment, and therapeutic interventions. For differential diagnoses, a variety of diagnostic techniques are used, such as brain imaging, EEG analysis, molecular analysis, and cognitive, psychological, and physical examination. This special issue collection welcomes high-quality original research and other types of articles on machine learning and deep learning for biomedical medical diagnosis and treatment.

Keywords

Medical imaging, human body imaging and therapy, data fusion techniques, imaging modality, computer-aided diagnosis, diagnostic imaging