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Zeinab Abboud

Doctorat - Polytechnique
Superviseur⋅e principal⋅e
Hervé Lombaert
Sujets de recherche
Apprentissage profond
IA et santé
Modèles bayésiens
Modèles probabilistes
Parcimonie
Réseaux de neurones profonds
Vision par ordinateur
GitHub

Publications

Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis
Zeinab Abboud
Hervé Lombaert
Samuel Kadoury
Efficiently quantifying predictive uncertainty in medical images remains a challenge. While Bayesian neural networks (BNN) offer predictive … (voir plus)uncertainty, they require substantial computational resources to train. Although Bayesian approximations such as ensembles have shown promise, they still suffer from high training and inference costs. Existing approaches mainly address the costs of BNN inference post-training, with little focus on improving training efficiency and reducing parameter complexity. This study introduces a training procedure for a sparse (partial) Bayesian network. Our method selectively assigns a subset of parameters as Bayesian by assessing their deterministic saliency through gradient sensitivity analysis. The resulting network combines deterministic and Bayesian parameters, exploiting the advantages of both representations to achieve high task-specific performance and minimize predictive uncertainty. Demonstrated on multi-label ChestMNIST for classification and ISIC, LIDC-IDRI for segmentation, our approach achieves competitive performance and predictive uncertainty estimation by reducing Bayesian parameters by over 95\%, significantly reducing computational expenses compared to fully Bayesian and ensemble methods.
2024-10-03
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org