Deep learning-based framework for slide-based histopathological image analysis.

ABSTRACT: Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major applications in digital pathology using machine learning include automatic cancer classification, survival analysis, and subtyping from pathological images. While most pathological image analyses are based on patch-wise processing due to the extremely large size of histopathology images, there are several applications that predict a single clinical outcome or perform pathological diagnosis per slide (e.g., cancer classification, survival analysis). However, current slide-based analyses are task-dependent, and a general framework of slide-based analysis in WSI has been seldom investigated. We propose a novel slide-based

SUBMITTER: Kosaraju S 

PROVIDER: S-EPMC9646838 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature