Manon Ansart

MCU – Computer Science, Permanent

Université Bourgogne Franche-Comté
Institut Marey - I3M
64 rue de Sully
21000 Dijon


Machine learning for medical usage

Curriculum Vitae

2021-today: Assistant professore at LEAD (Université de Bourgogne Franche Comté, CNRS UMR5022) and ESIREM, Dijon, France


2020-2021: Postdoc at the imaging and orthopedics research laboratory (LIO) of the École de Technologie Supérieure (ETS), at the research center of Montréal University (CRCHUM), Montréal, Québec, Canada. 

Topic: Statistical shape model for the automatic 3D reconstruction of the spine from radiographs. Partnership with EOS Imaging.


2016-2019: Doctorate at Aramis (Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Brain Institute (ICM) - Pitié-Salpêtrière hospital), Paris, France 

Topic: Design of data driven decision support systems for the early detection of subjects at risk to develop Alzheimer’s disease

Supervisors: Stanley Durrleman and Didier Dormont


2011-2016: Engineering studies at INSA Rouen. Major in computer systems and specialization in data mining.


  • Ansart, M., Epelbaum, S., Bassignana, G., Bône, A., Bottani, S., Cattai, T., Couronné, R., Faouzi, J., Koval, I., Louis, M., Thibeau-Sutre, E., Wen, J., Wild, A., Burgos, N., Dormont, D., Colliot, O., & Durrleman, S. (2021). Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Medical Image Analysis, 67, 101848. More ›
  • Ansart, M., Epelbaum, S., Houot, M., Nedelec, T., Lekens, B., Gantzer, L., Dormont, D., & Durrleman, S. (2021). Changes in the use of psychotropic drugs during the course of Alzheimer's disease: A large‐scale longitudinal study of French medical records. Alzheimer's & Dementia : Translational Research & Clinical Interventions, 7(1), e12210. More ›
  • Couvy-Duchesne, B., Faouzi, J., Martin, B., Thibeau–Sutre, E., Wild, A., Ansart, M., Durrleman, S., Dormont, D., Burgos, N., & Colliot, O. (2020). Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge. Frontiers in Psychiatry. More ›
  • Marinescu, R. V., Oxtoby, N. P., Young, A. L., Bron, E. E., Toga, A. W., Weiner, M. W., Barkhof, F., Fox, N. C., Eshaghi, A., Toni, T., Salaterski, M., Lunina, V., Ansart, M., Durrleman, S., Lu, P., Iddi, S., Li, D., Thompson, W. K., Donohue, M. C., Nahon, A., Levy, Y., Halbersberg, D., Cohen, M., Liao, H., Li, T., Yu, K., Zhu, H., Tamez-Pena, J. G., Ismail, A., Wood, T., Bravo, H. C., Nguyen, M., Sun, N., Feng, J., Yeo, B. T. T., Chen, G., Qi, K., Chen, S., Qiu, D., Buciuman, I., Kelner, A., Pop, R., Rimocea, D., Ghazi, M. M., Nielsen, M., Ourselin, S., Sorensen, L., Venkatraghavan, V., Liu, K., Rabe, C., Manser, P., Hill, S. M., Howlett, J., Huang, Z., Kiddle, S., Mukherjee, S., Rouanet, A., Taschler, B., Tom, B. D. M., White, S. R., Faux, N., Sedai, S., Oriol, J. de V., Clemente, E. E. V., Estrada, K., Aksman, L., Altmann, A., Stonnington, C. M., Wang, Y., Wu, J., Devadas, V., Fourrier, C., Raket, L. L., Sotiras, A., Erus, G., Doshi, J., Davatzikos, C., Vogel, J., Doyle, A., Tam, A., Diaz-Papkovich, A., Jammeh, E., Koval, I., Moore, P., Lyons, T. J., Gallacher, J., Tohka, J., Ciszek, R., Jedynak, B., Pandya, K., Bilgel, M., Engels, W., Cole, J., Golland, P., Klein, S., & Alexander, D. C. (2020). The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up. arXiv:2002.03419 [q-bio, stat]. More ›
  • Ansart, M., Epelbaum, S., Gagliardi, G., Colliot, O., Dormont, D., Dubois, B., Hampel, H., Durrleman, S., & for the Alzheimer’s Disease Neuroimaging Initiative* and the INSIGHT-preAD study (2019). Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis. Statistical Methods in Medical Research. More ›
  • Ansart, M., Epelbaum, S., Gagliardi, G., Colliot, O., Hampel, H., & Durrleman, S. (2017). Prediction of Amyloidosis from Neuropsychological and MRI Data for Cost Effective Inclusion of Pre-symptomatic Subjects in Clinical Trials. In (pp. 357-364). : Springer International Publishing. More ›