91 citations · 283 across the 17 of their papers we have counts for
19 papers
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…
Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation
Franco Matzkin, Agostina Larrazabal, Diego H Milone +2
Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclerosis. However, domain shifts, suc…
Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation
Estanislao Claucich, Sara Hooker, Diego H. Milone +2
Ensembling is commonly regarded as an effective way to improve the general performance of models in machine learning, while also increasing the robustness of predictions. When it c…
Comprehensive benchmarking of large language models for RNA secondary structure prediction
L. I. Zablocki, L. A. Bugnon, M. Gerard +3
Inspired by the success of large language models (LLM) for DNA and proteins, several LLM for RNA have been developed recently. RNA-LLM uses large datasets of RNA sequences to learn…
Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoders
Josefina Catoni, Domonkos Martos, Ferenc Csikor +5
Optimal computations under uncertainty require an adequate probabilistic representation about beliefs. Deep generative models, and specifically Variational Autoencoders (VAEs), hav…
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Nicolás Gaggion, Benjamin A. Matheson, Yan Xia +6
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volume…
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…
Multi-center anatomical segmentation with heterogeneous labels via landmark-based models
Nicolás Gaggion, Maria Vakalopoulou, Diego H. Milone +1
Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are…
Using segment-based features of jaw movements to recognize foraging activities in grazing cattle
José O. Chelotti, Sebastián R. Vanrell, Luciano S. Martinez-Rau +7
Precision livestock farming optimizes livestock production through the use of sensor information and communication technologies to support decision making, proactively and near rea…
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…
Impact of class imbalance on chest x-ray classifiers: towards better evaluation practices for discrimination and calibration performance
Candelaria Mosquera, Luciana Ferrer, Diego Milone +2
This work aims to analyze standard evaluation practices adopted by the research community when assessing chest x-ray classifiers, particularly focusing on the impact of class imbal…
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…
Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference
Rodrigo Echeveste, Enzo Ferrante, Diego H. Milone +1
Theories for autism spectrum disorder (ASD) have been formulated at different levels: ranging from physiological observations to perceptual and behavioral descriptions. Understandi…
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…
Inferring unknown biological function by integration of GO annotations and gene expression data
Guillermo Leale, Ariel Bayá, Diego Milone +2
Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been alread…
Blankets Joint Posterior score for learning Markov network structures
Federico Schlüter, Yanela Strappa, Diego H. Milone +1
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a do…
Screening of Obstructive Sleep Apnea with Empirical Mode Decomposition of Pulse Oximetry
Gastón Schlotthauer, Leandro E. Di Persia, Luis D. Larrateguy +1
Detection of desaturations on the pulse oximetry signal is of great importance for the diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help…