NLP/LLM Interest Group

Abstract: Deep learning models have achieved expert-level performance in diagnosing various ophthalmic conditions using imaging modalities like color fundus photography (CFP). However, these models operate under the assumption that the training and test data are drawn from an identical distribution1. When this assumption is violated by covariate shifts (e.g., varying imaging protocols, camera hardware, field-of-view differences, patient demographics), performance degrades substantially. Unsupervised Domain Adaptation (UDA) addresses this problem by adapting models using unlabeled target data. Existing UDA approaches typically align feature distributions using adversarial learning or entropy-based objectives driven by softmax probabilities. However, softmax normalizes logit magnitudes, which may obscure distributional shifts and cause falsely overconfident predictions. In this study, we propose Class-Conditional Energy Alignment, which adapts source-trained classifiers by matching energy computed directly from unnormalized logits across source and target domains.

Younjoon Chung is a Ph.D. student in Computational Biology and Biomedical Informatics (CBB) at Yale University, advised by Prof. Qingyu Chen and Prof. Lucila Ohno-Machado. His research interests lie in the intersection of machine learning, computer vision and healthcare. Specifically, focusing on developing robust domain adaptation techniques to ensure medical AI models can generalize across diverse clinical environments, including variations in patient populations, imaging hardware, etc.