Investigation of semi- and self-supervised learning methods in the histopathological domain.

ABSTRACT: Training models with semi- or self-supervised learning methods is one way to reduce annotation effort since they rely on unlabeled or sparsely labeled datasets. Such approaches are particularly promising for domains with a time-consuming annotation process requiring specialized expertise and where high-quality labeled machine learning datasets are scarce, like in computational pathology. Even though some of these methods have been used in the histopathological domain, there is, so far, no comprehensive study comparing different approaches. Therefore, this work compares feature extractors models trained with state-of-the-art semi- or self-supervised learning methods PAWS, SimCLR, and SimSiam within a unified framework. We show that such models, across different architectures and network con

SUBMITTER: Voigt B 

PROVIDER: S-EPMC10070179 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature