Domain-Adversarial Neural Networks to Explore Biases in the Diagnosis of Multiple Eye Conditions from Fundus Image Data

Chiara Pullega, Arianna Dagliati, Allan Tucker

Abstract: Deep learning (DL) algorithms have shown great promise in the medical field but face criticism for reflecting and perpetuating systemic biases in healthcare. This study addresses algorithmic biases in ophthalmology, focusing on the effects of demographic attributes like age and gender in DL analyses for eye disease classification.

External IDs:dblp:conf/aime/PullegaDT25