member

Previous page Olivier COLLIOT PhD, DR2, CNRS Team “Algorithms, Models and Methods for Images and Signals of the Human Brain ” https://www.aramislab.fr/perso/coll https://www.aramislab.fr https://twitter.com/https://twitter.com/oliviercolliot https://www.linkedin.com/in/olivier-colliot-8072235

Biography

Olivier Colliot is a Research Director (equivalent to Full Professor) at CNRS (Department of Computer Science and Signal Processing). He is the founding co-head of the ARAMIS team, a multidisciplinary group dedicated to the design of mathematical and computational methods to study brain diseases from multimodal data. He is an Editorial Board Member of Medical Image Analysis and the Conference Chair of SPIE Medical Imaging. He coordinates the development of the Open Source software platform Clinica (www.clinica.run). He teaches at the Master's level at Ecole Normale Supérieure de Paris-Saclay and Centrale-Supelec (Master Mathematics, Vision and Learning, course on deep learning for medical imaging), at University of Paris (Master Bioentrepreneur, course on introduction to artificial intelligence) and at Ecole des Mines de Paris (course on machine learning for brain imaging). His full publication list is available at https://cv.archives-ouvertes.fr/olivier-colliot

Research work

His research interests are: machine learning, medical image analysis and their application to the study of brain disorders.

Publications

  • Burgos N, Bottani S, Faouzi J, Thibeau-Sutre E, and Colliot O. Deep learning for brain disorders: from data processing to disease treatment. Briefings in Bioinformatics, 2021 Dec 15:bbaa310.
  • Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, and Colliot O, Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation, Medical Image Analysis, 63, 101694, 2020.
  • Wei W, Poirion E, Bodini B, Durrleman S, Ayache N, Stankoff B, and Colliot O, Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis, Medical Image Analysis, 58, 101546, 2019.
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert M-O, Durrleman S, Evgeniou T, and Colliot O, Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data, NeuroImage, 183:504–521, 2018.
  • Cuingnet R, Glaunès JA, Chupin M, Benali H, and Colliot O, The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (3), 682-696, 2013.