Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making

• Other Authors: Rahman, Mahmudur (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Medicine; machine learning; feature selection; osteoporosis; postmenopausal women; pre-screening; risk assessment; colorectal cancer; colon polyp; image features; convolutional neural network; artificial intelligence; radiomics; pancreatic imaging; MRI; CT; PET; acute lymphoblastic leukemia (ALL); blood smear; convolutional neural networks; deep learning; white blood cells; dysarthria; gated recurrent units; ordinal classification; multi-instance learning; weak supervision; breast cancer; key instance; uncertainty select; mammography; deep neural network; classification; HAM10000; skin lesion; ESRGAN; medical imaging; healthcare; decision making; cervical cancer; ensemble learning; Internet of Medical Things; oral cancer; biomedical imaging; Inception model; hybrid deep learning; COVID-19 CT-scan; 3D image segmentation; 3D UNet; 3D ResUNet; 3D VGGUNet; 3D DenseUNet; ultrasonic imaging; kidney; object detection; vision loss; diabetic retinopathy; image enhancement; APTOS; stand-alone artificial intelligence; radiology; benchmarking; population screening
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.