Quality control stress test for deep learning-based diagnostic model in digital pathology.

ABSTRACT: Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts' influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of

SUBMITTER: Schomig-Markiefka B 

PROVIDER: S-EPMC8592835 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature