Comparative analysis of high- and low-level deep learning approaches in microsatellite instability prediction.

ABSTRACT: Deep learning-based approaches in histopathology can be largely divided into two categories: a high-level approach using an end-to-end model and a low-level approach using feature extractors. Although the advantages and disadvantages of both approaches are empirically well known, there exists no scientific basis for choosing a specific approach in research, and direct comparative analysis of the two approaches has rarely been performed. Using the Cancer Genomic Atlas (TCGA)-based dataset, we compared these two different approaches in microsatellite instability (MSI) prediction and analyzed morphological image features associated with MSI. Our high-level approach was based solely on EfficientNet, while our low-level approach relied on LightGBM and multiple deep learning models trained on pu

SUBMITTER: Park J 

PROVIDER: S-EPMC9293930 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature