Uncertainty Quantification for Eosinophil Segmentation
This work addresses the need for efficient and precise medical imaging tools to aid pathologists in diagnosing EoE, an increasingly prevalent allergic condition, but it is incremental as it builds on an existing approach.
The paper tackles the problem of diagnosing Eosinophilic Esophagitis (EoE) by improving eosinophil segmentation in medical images, resulting in a method that provides uncertainty quantification to assist pathologists in identifying eosinophils.
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.