Search Results for author: Stanley Durrleman

Found 19 papers, 5 papers with code

Benchmarking off-the-shelf statistical shape modeling tools in clinical applications

no code implementations7 Sep 2020 Anupama Goparaju, Alexandre Bone, Nan Hu, Heath B. Henninger, Andrew E. Anderson, Stanley Durrleman, Matthijs Jacxsens, Alan Morris, Ibolya Csecs, Nassir Marrouche, Shireen Y. Elhabian

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.


Mixture of Conditional Gaussian Graphical Models for unlabelled heterogeneous populations in the presence of co-factors

1 code implementation19 Jun 2020 Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière

We demonstrate on synthetic and real data how this method fulfils its goal and succeeds in identifying the sub-populations where the Mixtures of GGM are disrupted by the effect of the co-features.

Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM

no code implementations23 Mar 2020 Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière

In this paper, we introduce a theoretical framework, with state-of-the-art convergence guarantees, for any deterministic approximation of the E step.

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.

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 Feb 2020 Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander

TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.

alzheimer's disease detection Disease Prediction

Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects

no code implementations5 Apr 2019 Igor Koval, Stéphanie Allassonnière, Stanley Durrleman

The ability to predict the progression of biomarkers, notably in NDD, is limited by the size of the longitudinal data sets, in terms of number of patients, number of visits per patients and total follow-up time.

Data Augmentation

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.


Parallel transport in shape analysis: a scalable numerical scheme

no code implementations23 Nov 2017 Maxime Louis, Alexandre Bône, Benjamin Charlier, Stanley Durrleman

The analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake.

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.

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.

Benchmarking Classification +1

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

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