This book deals with the recent advancements in computer vision techniques such as active learning, few-shot learning, zero shot learning, explainable and interpretable ML, online learning, AutoML etc. and their applications in medical domain. Moreover, the key challenges which affect the design, development, and performance of medical imaging systems are addressed. In addition, the state-of-the-art medical imaging methodologies for efficient, interpretable, explainable, and practical implementation of computer imaging techniques are discussed. At present, there are no textbook resources that address the medical imaging technologies. There are ongoing and novel research outcomes which would be useful for the development of novel medical imaging technologies/processes/equipment which can improve the current state of the art.

In recent years, AI/ML tools have become more prevalent in the fields of medical imaging and imaging informatics, where systems are already outperforming physicians in a range of domains, such as in the classification of retinal fundus images in ophthalmology, chest X-rays in radiology, and skin cancer detection in dermatology, among many others. It has recently emerged as one of the fastest growing research areas given the evolution of techniques in radiology, molecular imaging, anatomical imaging, and functional imaging for detection, segmentation, diagnosis, annotation, summarization, and prediction. The ongoing innovations in this exciting and promising field play a powerful role in influencing the lives of millions through health, safety, education, and other opportunities intended to be shared across all segments of society. To achieve further progress, this Special Issue (SI) invited both research and review-type manuscripts to showcase ongoing research progress and development based on applications of AI/ML (especially DL techniques) in medical imaging to influence human health and healthcare systems in the diagnostic decision-making process. The SI published fourteen articles after a rigorous peer-review process across the spectrum of medical imaging modalities and the diversity of specialties depending on imaging techniques from radiology, dermatology, pathology, colonoscopy, endoscopy, etc.