no code implementations • 25 Jan 2024 • Balamurali Murugesan, Sukesh Adiga Vasudeva, Bingyuan Liu, Hervé Lombaert, Ismail Ben Ayed, Jose Dolz
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare.
1 code implementation • 11 Mar 2023 • Balamurali Murugesan, Sukesh Adiga V, Bingyuan Liu, Hervé Lombaert, Ismail Ben Ayed, Jose Dolz
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain.
1 code implementation • 18 Jan 2023 • Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
The performance of learning-based algorithms improves with the amount of labelled data used for training.
2 code implementations • 16 Jan 2023 • Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
This paper proposes Test-time Augmentation for Active Learning (TAAL), a novel semi-supervised active learning approach for segmentation that exploits the uncertainty information offered by data transformations.
no code implementations • 11 Jan 2023 • Mohammad Karami, Hervé Lombaert, David Rivest-Hénault
The use of the physics-guided loss function in a deep learning model has led to a better generalization in the prediction of deformations in unseen simulation cases.
1 code implementation • 16 May 2022 • Mathilde Bateson, Hervé Lombaert, Ismail Ben Ayed
In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few or even a single subject(s).
no code implementations • 9 Aug 2021 • Benoit Anctil-Robitaille, Antoine Théberge, Pierre-Marc Jodoin, Maxime Descoteaux, Christian Desrosiers, Hervé Lombaert
The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to 8 times larger than those of T1w images.
1 code implementation • 6 Aug 2021 • Mathilde Bateson, Hoel Kervadec, Jose Dolz, Hervé Lombaert, Ismail Ben Ayed
Our method yields comparable results to several state of the art adaptation techniques, despite having access to much less information, as the source images are entirely absent in our adaptation phase.
1 code implementation • 1 Oct 2020 • Adrian Galdran, José Dolz, Hadi Chakor, Hervé Lombaert, Ismail Ben Ayed
Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space.
2 code implementations • 3 Sep 2020 • Adrian Galdran, André Anjos, José Dolz, Hadi Chakor, Hervé Lombaert, Ismail Ben Ayed
Our analysis demonstrates that the retinal vessel segmentation problem is far from solved when considering test images that differ substantially from the training data, and that this task represents an ideal scenario for the exploration of domain adaptation techniques.
1 code implementation • 8 Aug 2019 • Mathilde Bateson, Jose Dolz, Hoel Kervadec, Hervé Lombaert, Ismail Ben Ayed
We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions.