Search Results for author: Florence Forbes

Found 13 papers, 2 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.

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

Non-asymptotic model selection in block-diagonal mixture of polynomial experts models

no code implementations18 Apr 2021 TrungTin Nguyen, Faicel Chamroukhi, Hien Duy Nguyen, Florence Forbes

This model selection criterion allows us to handle the challenging problem of inferring the number of mixture components, the degree of polynomial mean functions, and the hidden block-diagonal structures of the covariance matrices, which reduces the number of parameters to be estimated and leads to a trade-off between complexity and sparsity in the model.

Model Selection regression

A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

1 code implementation6 Apr 2021 TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi, Florence Forbes

Mixture of experts (MoE) are a popular class of statistical and machine learning models that have gained attention over the years due to their flexibility and efficiency.

Model Selection

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

Conjugate Mixture Models for Clustering Multimodal Data

no code implementations9 Dec 2020 Vasil Khalidov, Florence Forbes, Radu Horaud

The algorithm and its variants are tested and evaluated within the task of 3D localization of several speakers using both auditory and visual data.

Model Selection

Rigid and Articulated Point Registration with Expectation Conditional Maximization

no code implementations9 Dec 2020 Radu Horaud, Florence Forbes, Manuel Yguel, Guillaume Dewaele, Jian Zhang

This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration.

Approximate Bayesian computation via the energy statistic

1 code implementation14 May 2019 Hien D. Nguyen, Julyan Arbel, Hongliang Lü, Florence Forbes

Furthermore, we propose a consistent V-statistic estimator of the energy statistic, under which we show that the large sample result holds, and prove that the rejection ABC algorithm, based on the energy statistic, generates pseudo-posterior distributions that achieves convergence to the correct limits, when implemented with rejection thresholds that converge to zero, in the finite sample setting.

EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis

no code implementations4 Sep 2015 Israel D. Gebru, Xavier Alameda-Pineda, Florence Forbes, Radu Horaud

We propose a model selection method based on a minimum message length criterion, provide a weight initialization strategy, and validate the proposed algorithms by comparing them with several state of the art parametric and non-parametric clustering techniques.

Model Selection

Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies

no code implementations30 Sep 2014 Antoine Deleforge, Florence Forbes, Sileye . Ba, Radu Horaud

This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations.


High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables

no code implementations10 Aug 2013 Antoine Deleforge, Florence Forbes, Radu Horaud

We introduce a mixture of locally-linear probabilistic mapping model that starts with estimating the parameters of inverse regression, and follows with inferring closed-form solutions for the forward parameters of the high-dimensional regression problem of interest.

Data Augmentation Dimensionality Reduction +1

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