91 citations · 205 across the 9 of their papers we have counts for
10 papers
Label-Free Foundational Model Selection for Medical Image Classification under Distribution Shift via Pseudo Label Discrepancy
Juan Iñaki Larrea, Lucas Mansilla, Enzo Ferrante
Foundation models are increasingly deployed for medical image analysis. However, under the inter-institutional distribution shift typical of deployment, their performance varies wi…
Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
María Florencia Acosta, Rodrigo García Arancibia, Pamela Llop +2
This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly inc…
BM-CL: Bias Mitigation through the lens of Continual Learning
Lucas Mansilla, Rodrigo Echeveste, Camila Gonzalez +2
Biases in machine learning pose significant challenges, particularly when models amplify disparities that affect disadvantaged groups. Traditional bias mitigation techniques often…
ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality
Matias Cosarinsky, Ramiro Billot, Lucas Mansilla +5
Assessing the quality of automatic image segmentation is crucial in clinical practice, but often very challenging due to the limited availability of ground truth annotations. Rever…
Unsupervised bias discovery in medical image segmentation
Nicolás Gaggion, Rodrigo Echeveste, Lucas Mansilla +2
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…
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…
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…
Domain Generalization via Gradient Surgery
Lucas Mansilla, Rodrigo Echeveste, Diego H. Milone +1
In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains. When the aim is to make pred…
Hybrid graph convolutional neural networks for landmark-based anatomical segmentation
Nicolás Gaggion, Lucas Mansilla, Diego Milone +1
In this work we address the problem of landmark-based segmentation for anatomical structures. We propose HybridGNet, an encoder-decoder neural architecture which combines standard…
Learning Deformable Registration of Medical Images with Anatomical Constraints
Lucas Mansilla, Diego H. Milone, Enzo Ferrante
Deformable image registration is a fundamental problem in the field of medical image analysis. During the last years, we have witnessed the advent of deep learning-based image regi…