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 • 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.
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 • 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 • 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 • 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 • 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.