CONF Ozbulak_MICCAI_2026/IDIAP Loss-Conditioned Utility-Fairness Boundary Modeling for Medical Imaging Özbulak, Gökhan Jimenez-del-Toro, Oscar Berton, Lilian Anjos, André EXTERNAL http://publications.idiap.ch/attachments/papers/2026/Ozbulak_MICCAI_2026.pdf PUBLIC Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention 2026 Medical image classifiers can achieve strong global performance while exhibiting different error profiles across sensitive groups. We propose a loss-conditioned framework for efficient utility-fairness boundary modeling in medical image classification. The method combines binary cross-entropy with a differentiable Equalized Odds loss and uses a two-dimensional preference vector to control their relative weights. Independently trained models define reference frontiers, while a single loss-conditioned backbone receives the same preference vector and is queried at both training and unseen preferences. We evaluate the framework on Harvard-GF-3300 glaucoma detection with gender as the sensitive attribute using ResNet-18, EfficientNet-B1, DenseNet-121, and ViT-Small. Conditioned models are trained with nine preference vectors and evaluated on denser aligned grids to assess interpolation. Across architectures, conditioned models approximate useful regions of independently trained ACC-EOD frontiers and enable dense aligned preference queries without additional optimization. These results support loss-conditioned learning as an efficient frontier-exploration tool rather than a single-point accuracy maximization method.