  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.
