65 citations · 160 across the 35 of their papers we have counts for
5 papers · 1 filter
Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to…
Trust your neighbours: Penalty-based constraints for model calibration
Balamurali Murugesan, Sukesh Adiga, Bingyuan Liu +3
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain. While recent literature on calib…
TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
Deep learning methods typically depend on the availability of labeled data, which is expensive and time-consuming to obtain. Active learning addresses such effort by prioritizing w…
Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning
Mohammad Karami, Hervé Lombaert, David Rivest-Hénault
Finite element methods (FEM) are popular approaches for simulation of soft tissues with elastic or viscoelastic behavior. However, their usage in real-time applications, such as in…
Active learning for medical image segmentation with stochastic batches
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image…