| Institution: | 1. Department of Nephrology, University Health Network, Toronto, Ontario, Canada
Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada;2. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada;3. Rhazes Lab, Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA |
| Abstract: | An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology. |
