Learning deformable registration of medical images with anatomical constraints. - Lucas Mansilla; Diego H. Milone; Enzo Ferrante (Neural Networks, 2020)

Domain Generalization via Gradient Surgery. - Lucas Mansilla; Rodrigo Echeveste; Diego H. Milone; Enzo Ferrante (ICCV, 2021)

Hybrid Graph Convolutional Neural Networks for Landmark-Based Anatomical Segmentation. - Nicolás Gaggion; Lucas Mansilla; Diego H. Milone; Enzo Ferrante (MICCAI (1), 2021)

Unsupervised Bias Discovery in Medical Image Segmentation. - Nicolás Gaggion; Rodrigo Echeveste; Lucas Mansilla; Diego H. Milone; Enzo Ferrante (CLIP/FAIMI/EPIMI@MICCAI, 2023)

CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images. - Nicolás Gaggion; Candelaria Mosquera; Lucas Mansilla; Martina Aineseder; Diego H. Milone; Enzo Ferrante (CoRR, 2023)

Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis. - Nicolás Gaggion; Lucas Mansilla; Candelaria Mosquera; Diego H. Milone; Enzo Ferrante (IEEE Trans. Medical Imaging, 2023)

Demographically-Informed Prediction Discrepancy Index: Early Warnings of Demographic Biases for Unlabeled Populations. - Lucas Mansilla; Estanislao Claucich; Rodrigo Echeveste; Diego H. Milone; Enzo Ferrante (Trans. Mach. Learn. Res., 2024)

In-Context Reverse Classification Accuracy: Efficient Estimation of Segmentation Quality without Ground-Truth. - Matias Cosarinsky; Ramiro Billot; Lucas Mansilla; Gabriel Gimenez; Nicolás Gaggion; Guanghui Fu; Enzo Ferrante (CoRR, 2025)