Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images.

ABSTRACT: Computational image analysis is one means for evaluating digitized histopathology specimens that can increase the reproducibility and reliability with which cancer diagnoses are rendered while simultaneously providing insight as to the underlying mechanisms of disease onset and progression. A major challenge that is confronted when analyzing samples that have been prepared at disparate laboratories and institutions is that the algorithms used to assess the digitized specimens often exhibit heterogeneous staining characteristics because of slight differences in incubation times and the protocols used to prepare the samples. Unfortunately, such variations can render a prediction model learned from one batch of specimens ineffective for characterizing an ensemble originating from another site

SUBMITTER: Ren J 

PROVIDER: S-EPMC6529804 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature