no code implementations • 26 Jan 2021 • Benjamin Lambert, Maxime Louis, Senan Doyle, Florence Forbes, Michel Dojat, Alan Tucholka
Learning is performed using a multicentric dataset of healthy brain MRIs, and segmentation performances are estimated on White-Matter Hyperintensities and tumors lesions.
no code implementations • 14 Sep 2021 • Stenzel Cackowski, Emmanuel L. Barbier, Michel Dojat, Thomas Christen
ImUnity is an original deep-learning model designed for efficient and flexible MR image harmonization.
no code implementations • 25 Oct 2021 • Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes, Carole Lartizen, Michel Dojat
Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identification of barely visible brain lesions, especially given the lack of annotated datasets.
no code implementations • 22 Sep 2022 • Benjamin Lambert, Florence Forbes, Senan Doyle, Alan Tucholka, Michel Dojat
Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images.
no code implementations • 3 Oct 2022 • Lucrezia Carboni, Michel Dojat, Sophie Achard
Comparisons between generative models and real networks combining two different nodal statistics reveal the complexity of human brain functional connectivity with differences at both global and nodal levels.
no code implementations • 5 Oct 2022 • Benjamin Lambert, Florence Forbes, Alan Tucholka, Senan Doyle, Harmonie Dehaene, Michel Dojat
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature.
no code implementations • 10 Nov 2022 • Benjamin Lambert, Florence Forbes, Senan Doyle, Alan Tucholka, Michel Dojat
In this work, we evaluate various uncertainty frameworks to detect OOD inputs in the context of Multiple Sclerosis lesions segmentation.
no code implementations • 27 Feb 2023 • Nicolas Pinon, Geoffroy Oudoumanessah, Robin Trombetta, Michel Dojat, Florence Forbes, Carole Lartizien
Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesions.
no code implementations • 28 Jul 2023 • Benjamin Lambert, Florence Forbes, Senan Doyle, Michel Dojat
The volume of a brain lesion (e. g. infarct or tumor) is a powerful indicator of patient prognosis and can be used to guide the therapeutic strategy.
no code implementations • 23 Aug 2023 • Benjamin Lambert, Pauline Roca, Florence Forbes, Senan Doyle, Michel Dojat
In this work we propose to compare two different pipelines based on anisotropic models to obtain the segmentation of the liver and tumors.
1 code implementation • 31 May 2023 • Dwight Nwaigwe, Lucrezia Carboni, Martial Mermillod, Sophie Achard, Michel Dojat
We introduce a novel approach of detection and interpretation of adversarial attacks from a graph perspective.
1 code implementation • 28 Jul 2023 • Benjamin Lambert, Florence Forbes, Senan Doyle, Michel Dojat
Deep Learning models are easily disturbed by variations in the input images that were not observed during the training stage, resulting in unpredictable predictions.
1 code implementation • 4 Sep 2023 • Geoffroy Oudoumanessah, Carole Lartizien, Michel Dojat, Florence Forbes
This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients.