Search Results for author: Benjamin Lambert

Found 7 papers, 1 papers with code

Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification

no code implementations23 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.

Segmentation Tumor Segmentation +1

Multi-layer Aggregation as a key to feature-based OOD detection

1 code implementation28 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.

TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images

no code implementations28 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.

Segmentation

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 +3

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.

Uncertainty Quantification

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.

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 Segmentation +1

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