Chest x-ray automated triage: A semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures. - Candelaria Mosquera; Facundo Nahuel Diaz; Fernando Binder; Jose Martin Rabellino; Sonia Elizabeth Benítez; Alejandro Daniel Beresñak; Alberto Seehaus; Gabriel Ducrey; Jorge Alberto Ocantos; Daniel Roberto Luna (Comput. Methods Programs Biomed., 2021)

Understanding the impact of class imbalance on the performance of chest x-ray image classifiers. - Candelaria Mosquera; Luciana Ferrer; Diego H. Milone; Daniel R. Luna; Enzo Ferrante (CoRR, 2021)

User Satisfaction with an AI System for Chest X-Ray Analysis Implemented in a Hospital's Emergency Setting. - Diego Rabinovich; Candelaria Mosquera; Pierina Torrens; Martina Aineseder; Sonia E. Benitez (MIE, 2022)

Towards Unraveling Calibration Biases in Medical Image Analysis. - María Agustina Ricci Lara; Candelaria Mosquera; Enzo Ferrante; Rodrigo Echeveste (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)