Applied machine learning in hematopathology

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.

Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization

“…ML greatly improves work efficiency and accuracy. Driven by increasing computer power and algorithmic advances, ML has become a powerful tool for finding data patterns 24 , and it is widely applied in various fields, such as biology 25 , 26 , medicine 27 , 28 , healthcare 29 , environment 30 , fuel cells 31 and energy 32 . ML methods can also be used to develop new AMPs.…”