Utilizing Machine Learning in Pathology
By Lynn McCain | September 11 2020I
n a recently published Nature Reviews-Nephrology review article, “Digital pathology and computational image analysis in nephropathology,” Dr. Ulysses G. J. Balis discussed how the emergence of digital pathology — an image-based environment for the acquisition, management, and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. “In particular,” Balis explained, “by virtue of our newfound ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis, and integration of pathology data with other domains.”
Pathology is integrating machine-learning technology, which now allows for the extraction of information from image data, so tissues can be examined in an entirely new manner. These new tools are, according to Balis, “defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.”
ON THE COVER
ON THE COVER
ON THE COVER
ON THE COVER
ON THE COVER
ON THE COVER
ON THE COVER
ON THE COVER
ON THE COVER
ON THE COVER
