Varsha Himthani

Automatic glaucoma screening degrades when a model moves to another hospital and camera; a neuro-symbolic framework combines anatomical graph reasoning with differentiable fuzzy clinical rules in one network, trained on 9,100 fundus images drawn from a corpus of 12,619 images across eight datasets in eight countries and tested on 485 fully held-out RIM-ONE images from a different Spanish hospital and camera. On the in-distribution test set, EfficientNet-B4 with a graph attention network reaches an AUC of 0.9612 (95% CI 0.951–0.972) versus 0.9472 for EfficientNet-B4 alone, yet the combined full model scores 0.6155 cross-hospital, 8.1 points below the baseline.

Automatic glaucoma screening degrades when a model moves to another hospital and camera; a neuro-symbolic framework combines anatomical graph reasoning with differentiable fuzzy clinical rules in one network, trained on 9,100 fundus images drawn from a corpus of 12,619 images across eight datasets in eight countries and tested on 485 fully held-out RIM-ONE images from a different Spanish hospital and camera. On the in-distribution test set, EfficientNet-B4 with a graph attention network reaches an AUC of 0.9612 (95% CI 0.951–0.972) versus 0.9472 for EfficientNet-B4 alone, yet the combined full model scores 0.6155 cross-hospital, 8.1 points below the baseline.