%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Ozbulak_MICCAI_2026,
                      author = {{\"{O}}zbulak, G{\"{o}}khan and Jimenez-del-Toro, Oscar and Berton, Lilian and Anjos, Andr{\'{e}}},
                    projects = {FAIRMI},
         mainresearchprogram = {AI for Everyone},
  additionalresearchprograms = {AI for Life},
                       month = sep,
                       title = {Loss-Conditioned Utility-Fairness Boundary Modeling for Medical Imaging},
                   booktitle = {Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention},
                        year = {2026},
                    abstract = {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.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2026/Ozbulak_MICCAI_2026.pdf}
}