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Found 21 papers, 5 papers with code
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators.
PASOA- PArticle baSed Bayesian Optimal Adaptive design
This novel combination of stochastic optimization and tempered SMC allows to jointly handle design optimization and parameter inference.
Towards frugal unsupervised detection of subtle abnormalities in medical imaging
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.
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification
In this work we propose to compare two different pipelines based on anisotropic models to obtain the segmentation of the liver and tumors.
TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images
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.
Multi-layer Aggregation as a key to feature-based OOD detection
Deep Learning models are easily disturbed by variations in the input images that were not observed during the training stage, resulting in unpredictable predictions.
Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
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
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients
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.
Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation
In this work, we evaluate various uncertainty frameworks to detect OOD inputs in the context of Multiple Sclerosis lesions segmentation.
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
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
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
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.
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models
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.
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models
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.
Leveraging 3D Information in Unsupervised Brain MRI Segmentation
Learning is performed using a multicentric dataset of healthy brain MRIs, and segmentation performances are estimated on White-Matter Hyperintensities and tumors lesions.
Conjugate Mixture Models for Clustering Multimodal Data
The algorithm and its variants are tested and evaluated within the task of 3D localization of several speakers using both auditory and visual data.
Rigid and Articulated Point Registration with Expectation Conditional Maximization
This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration.
Approximate Bayesian computation via the energy statistic
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
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.
Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies
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
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.
