no code implementations • 10 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.
no code implementations • 30 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.
no code implementations • 4 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.
1 code implementation • 14 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.
no code implementations • 9 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.
no code implementations • 9 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.
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.
1 code implementation • 6 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.
no code implementations • 18 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.
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 • 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.
1 code implementation • NeurIPS 2023 • Yuchen Bai, Jean-Baptiste Durand, Grégoire Vincent, Florence Forbes
In such a context, discriminating leaf points from wood points presents a significant challenge due in particular to strong class imbalance and spatially irregular sampling intensity.
Ranked #1 on LIDAR Semantic Segmentation on ULS labeled data
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 • 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 • 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.
no code implementations • 11 Feb 2024 • Jacopo Iollo, Christophe Heinkelé, Pierre Alliez, Florence Forbes
This novel combination of stochastic optimization and tempered SMC allows to jointly handle design optimization and parameter inference.
no code implementations • 12 Mar 2024 • Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini
Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators.