Search Results for author: Michel Dojat

Found 7 papers, 0 papers with code

Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation

no code implementations10 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.

Image Segmentation Medical Image Segmentation +2

Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

no code implementations5 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.

Nodal statistics-based equivalence relation for graph collections

no code implementations3 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.

Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust

no code implementations22 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.

Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients

no code implementations25 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.

Unsupervised Anomaly Detection

Leveraging 3D Information in Unsupervised Brain MRI Segmentation

no code implementations26 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.

MRI segmentation Unsupervised Anomaly Detection

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