Artificial Intelligence in Pathological Image Analysis - Hardcover

by Masayuki Tsuneki (Guest Editor)

In routine pathological diagnosis, histopathological and cytopathological examination of specimens is conventionally performed under light microscopy. Whole slide images (WSIs) are the digitized counterparts of conventional glass slides obtained via specialized scanning devices. In recent years, the introduction of digital pathology into clinical workflows such as intraoperative consultations and secondary consultations is increasing steadily. The advent of WSIs has led to the application of medical image analysis, machine learning, and deep learning approaches for aiding pathologists in inspecting WSIs and routine diagnosis. Deep learning in particular has found a wide array of applications (e.g., classification, segmentation, and patient outcome predictions) in computational pathology. In a time of distinct paradigm shifts and novel technological innovations, it is necessary for us to establish a unified comprehension(s) of artificial intelligence (AI) approaches in experimental and clinical pathology. In this Special Issue entitled "Artificial Intelligence in Pathological Image Analysis", we collected a review and thirteen research articles in the areas of AI models in clinical and experimental pathology and computer vision in pathological image analysis. The published studies in this Special Issue provide great insights into the latest knowledge about the application of AI for pathological image analysis.