Using multi-label ensemble CNN classifiers to mitigate labelling inconsistencies in patch-level Gleason grading.

ABSTRACT: This paper presents a novel approach to enhance the accuracy of patch-level Gleason grading in prostate histopathology images, a critical task in the diagnosis and prognosis of prostate cancer. This study shows that the Gleason grading accuracy can be improved by addressing the prevalent issue of label inconsistencies in the SICAPv2 prostate dataset, which employs a majority voting scheme for patch-level labels. We propose a multi-label ensemble deep-learning classifier that effectively mitigates these inconsistencies and yields more accurate results than the state-of-the-art works. Specifically, our approach leverages the strengths of three different one-vs-all deep learning models in an ensemble to learn diverse features from the histopathology images to individually indicate the presenc

SUBMITTER: Butt MA 

PROVIDER: S-EPMC11226137 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature