Portrait de Margaux Luck

Margaux Luck

margaux.luck@mila.quebec
Scientifique senior en recherche appliquée, Recherche appliquée en apprentissage automatique

Publications

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links
Alex Fedorov
Eloy Geenjaar
Lei Wu
Tristan Sylvain
Thomas P. DeRamus
Margaux Luck
Maria Misiura
Girish Mittapalle
(Rex) Devon Hjelm
Sergey Plis
Vince D. Calhoun
2023-12-16
NeuroImage (publié)
doi.org
CMIM: Cross-Modal Information Maximization For Medical Imaging
Tristan Sylvain
Francis Dutil
Tess Berthier
Lisa Di Jorio
Margaux Luck
(Rex) Devon Hjelm
Yoshua Bengio
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as th… (voir plus)e different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.) and their associated radiology reports. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time.In this paper, we propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time, using recent advances in mutual information maximization. By maximizing cross-modal information at train time, we are able to outperform several state-of-the-art baselines in two different settings, medical image classification, and segmentation. In particular, our method is shown to have a strong impact on the inference-time performance of weaker modalities.
2021-06-06
IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)
doi.org
Exploring the Wasserstein metric for survival analysis
Tristan Sylvain
Margaux Luck
Joseph Paul Cohen
Andrea Lodi
Yoshua Bengio
Survival analysis is a type of semi-supervised task where the target output (the survival time) is often right-censored. Utilizing this info… (voir plus)rmation is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions can take advantage of this information: partial likelihood methods, rank methods, and our own classification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier (KM) estimate of the probability density to impute the labels of censored examples. The proposed method predicts the probability distribution of an event, letting us compute survival curves and expected times of survival that are easier to interpret than the rank. We also demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for survival models.
2021-01-01
(publié)
www.semanticscholar.org
Exploring the Wasserstein metric for time-to-event analysis.
Tristan Sylvain
Margaux Luck
Joseph Paul Cohen
Heloise Cardinal
Andrea Lodi
Yoshua Bengio
2021-01-01
SPACA (publié)
proceedings.mlr.press
Cross-Modal Information Maximization for Medical Imaging: CMIM
Tristan Sylvain
Francis Dutil
Tess Berthier
Lisa Di Jorio
Margaux Luck
(Rex) Devon Hjelm
Yoshua Bengio
2020-10-20
ArXiv (prépublication)
arxiv.org
arxiv.org