Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning.

ABSTRACT:

Background

The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model prediction accuracy without assessing the confidence in the predictions.

Methods

We propose a semantic segmentation model using Bayesian representation to segment nuclei from the histopathology images and to further quantify the epistemic uncertainty. We employ Bayesian approximation with Monte-Carlo (MC) dropout during the inference time to estimate the model's prediction uncertainty.

Results

We evaluate the performance of the proposed approach on the PanNuke dataset, which consists of 312 visual fields from 19 organ types. W

SUBMITTER: Gudhe NR 

PROVIDER: S-EPMC10585914 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature