71 citations · 119 across the 5 of their papers we have counts for
4 papers · 1 filter
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
Nicolás Gaggion, Candelaria Mosquera, Lucas Mansilla +4
The development of successful artificial intelligence models for chest X-ray analysis relies on large, diverse datasets with high-quality annotations. While several databases of ch…
Towards unraveling calibration biases in medical image analysis
María Agustina Ricci Lara, Candelaria Mosquera, Enzo Ferrante +1
In recent years the development of artificial intelligence (AI) systems for automated medical image analysis has gained enormous momentum. At the same time, a large body of work ha…
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
Nicolás Gaggion, Lucas Mansilla, Candelaria Mosquera +2
Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These m…
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 +7
BACKGROUND AND OBJECTIVES: The multiple chest x-ray datasets released in the last years have ground-truth labels intended for different computer vision tasks, suggesting that perfo…