Search Results for author: Olivier Colliot

Found 25 papers, 4 papers with code

Reproducibility in machine learning for medical imaging

no code implementations12 Sep 2022 Olivier Colliot, Elina Thibeau-Sutre, Ninon Burgos

Reproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge.

Interpretability of Machine Learning Methods Applied to Neuroimaging

no code implementations14 Apr 2022 Elina Thibeau-Sutre, Sasha Collin, Ninon Burgos, Olivier Colliot

Here, we aim at providing answers to these questions by presenting the most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context.

BIG-bench Machine Learning

The impact of aging on human brain network target controllability

no code implementations14 Dec 2021 Giulia Bassignana, Giordano Lacidogna, Paolo Bartolomeo, Olivier Colliot, Fabrizio De Vico Fallani

Understanding how few distributed areas can steer large-scale brain activity is a fundamental question that has also practical implications, which range from inducing specific patterns of behavior to counteracting disease.

Axial multi-layer perceptron architecture for automatic segmentation of choroid plexus in multiple sclerosis

1 code implementation8 Sep 2021 Marius Schmidt-Mengin, Vito A. G. Ricigliano, Benedetta Bodini, Emanuele Morena, Annalisa Colombi, Mariem Hamzaoui, Arya Yazdan Panah, Bruno Stankoff, Olivier Colliot

The conclusions of our paper are two-fold: 1) the studied deep learning methods could be useful tools to study CP in large cohorts of MS patients; 2)~Axial-MLP is a potentially viable alternative to convolutional neural networks for such tasks, although it could benefit from further improvements.

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

no code implementations16 Apr 2021 Simona Bottani, Ninon Burgos, Aurélien Maire, Adam Wild, Sebastian Ströer, Didier Dormont, Olivier Colliot

In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse.

Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort

no code implementations19 Mar 2020 Alexandre Morin, Jorge Samper-González, Anne Bertrand, Sebastian Stroer, Didier Dormont, Aline Mendes, Pierrick Coupé, Jamila Ahdidan, Marcel Lévy, Dalila Samri, Harald Hampel, Bruno Dubois, Marc Teichmann, Stéphane Epelbaum, Olivier Colliot

Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader$^{TM}$); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists.

General Classification

Step-wise target controllability identifies dysregulated pathways of macrophage networks in multiple sclerosis

no code implementations19 Mar 2020 Giulia Bassignana, Jennifer Fransson, Vincent Henry, Olivier Colliot, Violetta Zujovic, Fabrizio De Vico Fallani

In many applications it is often essential to test the ability of an individual node to control a specific target subset of the network.

Gaussian Graphical Model exploration and selection in high dimension low sample size setting

no code implementations11 Mar 2020 Thomas Lartigue, Simona Bottani, Stephanie Baron, Olivier Colliot, Stanley Durrleman, Stéphanie Allassonnière

We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one.

Visualization approach to assess the robustness of neural networks for medical image classification

no code implementations19 Nov 2019 Elina Thibeau Sutre, Olivier Colliot, Didier Dormont, Ninon Burgos

We demonstrated that the areas identified by the CNN were consistent with what is known of Alzheimer's disease and that the visualization approach extract coherent longitudinal patterns.

General Classification Image Classification +1

Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms

no code implementations CVPR 2018 Alexandre Bône, Olivier Colliot, Stanley Durrleman

We propose a method to learn a distribution of shape trajectories from longitudinal data, i. e. the collection of individual objects repeatedly observed at multiple time-points.


Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline

no code implementations23 Nov 2017 Alexandre Bône, Maxime Louis, Alexandre Routier, Jorge Samper, Michael Bacci, Benjamin Charlier, Olivier Colliot, Stanley Durrleman

We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data.

Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data

no code implementations10 Oct 2017 Pascal Lu, Olivier Colliot

Furthermore, we propose a framework allowing to combine several penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a $L_2$-penalty on imaging modalities.

Additive models General Classification

Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks

no code implementations25 Sep 2017 Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier, Michael Bacci, Olivier Colliot, Stéphanie Allassonnière, Stanley Durrleman

Model parameters show the variability of this average pattern of atrophy in terms of trajectories across brain regions, age at disease onset and pace of propagation.

Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease

no code implementations21 Sep 2017 Jorge Samper-González, Ninon Burgos, Sabrina Fontanella, Hugo Bertin, Marie-Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot

The core components are: 1) code to automatically convert the full ADNI database into BIDS format; 2) a modular architecture based on Nipype in order to easily plug-in different classification and feature extraction tools; 3) feature extraction pipelines for MRI and PET data; 4) baseline classification approaches for unimodal and multimodal features.

Classification General Classification

White Matter Fiber Segmentation Using Functional Varifolds

no code implementations18 Sep 2017 Kuldeep Kumar, Pietro Gori, Benjamin Charlier, Stanley Durrleman, Olivier Colliot, Christian Desrosiers

We use it to cluster fibers with a dictionary learning and sparse coding-based framework, and present a preliminary analysis using HCP data.

Dictionary Learning

Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI

no code implementations18 Sep 2017 Kuldeep Kumar, Laurent Chauvin, Mathew Toews, Olivier Colliot, Christian Desrosiers

So far, fingerprinting studies have focused on identifying features from single-modality MRI data, which capture individual characteristics in terms of brain structure, function, or white matter microstructure.

Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging

no code implementations19 Jul 2017 Emilie Gerardin, Gaël Chételat, Marie Chupin, Rémi Cuingnet, Béatrice Desgranges, Ho-Sung Kim, Marc Niethammer, Bruno Dubois, Stéphane Lehéricy, Line Garnero, Francis Eustache, Olivier Colliot

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features.

Classification General Classification

Robust imaging of hippocampal inner structure at 7T: in vivo acquisition protocol and methodological choices

no code implementations9 May 2016 Linda Marrakchi-Kacem, Alexandre Vignaud, Julien Sein, Johanne Germain, Thomas R Henry, Cyril Poupon, Lucie Hertz-Pannier, Stéphane Lehéricy, Olivier Colliot, Pierre-François Van de Moortele, Marie Chupin

Multi-slab registration yielded high quality datasets in 96 % of the subjects, thus compatible with further analyses of hippocampal inner structure. CONCLUSION:Multi-slab acquisition and registration setting is efficient for reducing acquisition time and consequently motion artifacts for ultra-high resolution imaging of the inner structure of the hippocampus.


Spatial and anatomical regularization of SVM for brain image analysis

no code implementations NeurIPS 2010 Remi Cuingnet, Marie Chupin, Habib Benali, Olivier Colliot

We show that Laplacian regularization provides a flexible framework to integrate various types of constraints and can be applied to both cortical surfaces and 3D brain images.

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