Uncertainty Quantification for Eosinophil Segmentation

TL;DR

This paper enhances eosinophil segmentation in medical images by integrating Monte Carlo Dropout to quantify uncertainty, aiding pathologists in diagnosis and improving model interpretability.

Contribution

It introduces a novel application of Monte Carlo Dropout for uncertainty quantification in eosinophil segmentation, building on prior deep learning methods.

Findings

Uncertainty visualization improves model performance assessment.

Method aids pathologists in identifying eosinophils.

Enhanced segmentation accuracy demonstrated.

Abstract

Eosinophilic Esophagitis (EoE) is an allergic condition increasing in prevalence. To diagnose EoE, pathologists must find 15 or more eosinophils within a single high-power field (400X magnification). Determining whether or not a patient has EoE can be an arduous process and any medical imaging approaches used to assist diagnosis must consider both efficiency and precision. We propose an improvement of Adorno et al's approach for quantifying eosinphils using deep image segmentation. Our new approach leverages Monte Carlo Dropout, a common approach in deep learning to reduce overfitting, to provide uncertainty quantification on current deep learning models. The uncertainty can be visualized in an output image to evaluate model performance, provide insight to how deep learning algorithms function, and assist pathologists in identifying eosinophils.