Diffusion and Multi-Domain Adaptation Methods for Eosinophil Segmentation

Eosinophilic Esophagitis (EoE) represents a challenging condition for medical providers today. The cause is currently unknown, the impact on a patient's daily life is significant, and it is increasing in prevalence. Traditional approaches for medical image diagnosis such as standard deep learning algorithms are limited by the relatively small amount of data and difficulty in generalization. As a response, two methods have arisen that seem to perform well: Diffusion and Multi-Domain methods with current research efforts favoring diffusion methods. For the EoE dataset, we discovered that a Multi-Domain Adversarial Network outperformed a Diffusion based method with a FID of 42.56 compared to 50.65. Future work with diffusion methods should include a comparison with Multi-Domain adaptation methods to ensure that the best performance is achieved.

Uncertainty Quantification for Eosinophil Segmentation

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.