1 code implementation • 1 Sep 2023 • Nicolás Gaggion, Rodrigo Echeveste, Lucas Mansilla, Diego H. Milone, Enzo Ferrante
It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected attributes like sex or ethnicity.
1 code implementation • 6 Jul 2023 • Nicolás Gaggion, Candelaria Mosquera, Lucas Mansilla, Julia Mariel Saidman, Martina Aineseder, Diego H. Milone, Enzo Ferrante
To address this gap, we introduce an extensive chest X-ray multi-center segmentation dataset with uniform and fine-grain anatomical annotations for images coming from five well-known publicly available databases: ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in 657, 566 segmentation masks.
2 code implementations • 21 Mar 2022 • Nicolás Gaggion, Lucas Mansilla, Candelaria Mosquera, Diego H. Milone, Enzo Ferrante
To this end, we introduce HybridGNet, an encoder-decoder neural architecture that leverages standard convolutions for image feature encoding and graph convolutional neural networks (GCNNs) to decode plausible representations of anatomical structures.
1 code implementation • ICCV 2021 • Lucas Mansilla, Rodrigo Echeveste, Diego H. Milone, Enzo Ferrante
In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains.
1 code implementation • 17 Jun 2021 • Nicolás Gaggion, Lucas Mansilla, Diego Milone, Enzo Ferrante
In this work we address the problem of landmark-based segmentation for anatomical structures.
2 code implementations • 20 Jan 2020 • Lucas Mansilla, Diego H. Milone, Enzo Ferrante
Deformable image registration is a fundamental problem in the field of medical image analysis.