State of machine and deep learning in histopathological applications in digestive diseases

• Published in: World journal of gastroenterology : WJG Vol. 27; no. 20; pp. 2545 - 2575
• Main Authors: Kobayashi, Soma, Saltz, Joel H, Yang, Vincent W
• Format: Journal Article
• Language: English
• Published: United States Baishideng Publishing Group Inc 28-05-2021
• Subjects: Algorithms; Deep Learning; Humans; Machine Learning; Minireviews; Deep learning; Histopathology; Artificial intelligence; Hepatology; Gastroenterology; Machine learning
• ISSN: 1007-9327; 2219-2840
• DOI: 10.3748/wjg.v27.i20.2545

Machine learning (ML)- and deep learning (DL)-based imaging modalities have exhibited the capacity to handle extremely high dimensional data for a number of computer vision tasks. While these approaches have been applied to numerous data types, this capacity can be especially leveraged by application on histopathological images, which capture cellular and structural features with their high-resolution, microscopic perspectives. Already, these methodologies have demonstrated promising performance in a variety of applications like disease classification, cancer grading, structure and cellular localizations, and prognostic predictions. A wide range of pathologies requiring histopathological evaluation exist in gastroenterology and hepatology, indicating these as disciplines highly targetable for integration of these technologies. Gastroenterologists have also already been primed to consider the impact of these algorithms, as development of real-time endoscopic video analysis software has been an active and popular field of research. This heightened clinical awareness will likely be important for future integration of these methods and to drive interdisciplinary collaborations on emerging studies. To provide an overview on the application of these methodologies for gastrointestinal and hepatological histopathological slides, this review will discuss general ML and DL concepts, introduce recent and emerging literature using these methods, and cover challenges moving forward to further advance the field.