Carrier-Vallières, Martin

Professeur, Faculté des sciences
FAC. SCIENCES Informatique

Coordonnées

Courriel


819-821-8000, poste 65116

Diplômes

(2017) Doctorate (Doctor of Philosophy - Medical Physics). McGill University.

(2012) Master's Thesis (Masters of Science - Medical Physics). McGill University.

(2010) Bachelor's (Baccalauréat en Ingénierie - Génie physique). École Polytechnique de Montréal.

Expérience académique

(2020) Assistant Professor. Université de Sherbrooke.

(2018-2020) Postdoctoral researcher. McGill University.

(2018-2019) Postdoctoral researcher. University of California, San Francisco.

(2017-2018) Postdoctoral researcher. INSERM UMR 1101, Brest, France.

Présentation

Sujets de recherche

Computer Science and Statistics.

Disciplines de recherche

Computer Science, Oncology.

Mots-clés

medical image analysis, machine learning, graph neural networks, natural language processing, distributed learning from federated databases.

Intérêts de recherche

Martin Vallières is devoting much of his current work to the development of a solution for the integrative modeling of heterogeneous medical data. He leads the development of MEDomicsLab, an open source platform for end-to-end computation in precision medicine. This platform will model heterogeneous data from hospitals using deep learning and machine learning methods based on graph theory. By contributing to the improvement of prediction models in medicine, MEDomicsLab will become a key artificial intelligence tool in the clinic.

Recherche clinique

Oui

Centre de recherche

Centre de recherche du CHUS

Langues parlées et écrites

Anglais, Français

Prix et distinctions

  • 1st prize: Rising Star in Medical Physics Symposium 2015. Medical Physics Research Training Network (MPRTN). (Prize / Award).
  • Mention d’Excellence - Baccalauréat. École Polytechnique de Montréal. (Honor).
  • Michael S. Patterson Publication Impact Prize in Medical Physics 2021. Canadian Organization of Medical Physicists. (Prize / Award).
  • Roblat Medal - 2018 citation prize. Physics in Medicine & Biology (PMB) journal. (Prize / Award).
  • Top 100 - Read Articles - 2017. Scientific Reports journal. (Distinction).

Financement

Grant. (Awarded). Principal Investigator. Multilevel graphical modeling of heterogeneous healthcare data in a federated learning setting. Natural Sciences and Engineering Research Council of Canada (NSERC). Discovery Grants. 132500 $ (2021-2026).

Research Chair. (Awarded). Principal Investigator. Development of an open-source computation platform for multi-omics data modeling in oncology. Canada CIFAR AI Chair, Mila. CIFAR Pan-Canadian Artificial Intelligence Strategy. 250000 $ (2020-2025).

Grant. (Awarded). Principal Investigator. Start-up funds, Faculty of Science. University of Sherbrooke. Start-up funds. 30000 $ (2020-2025).

Grant. (Awarded). Principal Investigator. Start-up funds, University Hospital (CHUS). Centre de Recherche du Centre Hospitalier de l'Université de Sherbrooke Inc. (CRCHUS) (Sherbrooke, QC). Start-up funds. 40000 $ (2020-2025).

Grant. (Awarded). Principal Investigator. Development of Artificial Intelligence Techniques for Automated Electric Power Asset Identification. InnovÉÉ - Innovation en énergie électrique. 66200 $ (2021-2022).

Grant. (Awarded). Principal Investigator. Development of Artificial Intelligence Techniques for Automated Electric Power Asset Identification. Natural Sciences and Engineering Research Council of Canada (NSERC). Alliance Grants (ALLRP). 66200 $ (2021-2022).

Grant. (Awarded). Co-investigator. Université de Sherbrooke : Centre intégré universitaire de santé et de services sociaux de l’Estrie – Centre hospitalier universitaire de Sherbrooke (CIUSSS de l’Estrie – CHUS). PROMPT-Québec. Financement en IA – Volet Propulsion des universités. 500000 $ (2020-2022).

Grant. (Completed). Principal Investigator. Energ-AI: Artificial Intelligence in Electrical Power Engineering. Mathematics of Information Technology and Complex Systems (MITACS). Mitacs Accelerate. 15000 $ (2020).

Grant. (Completed). Co-applicant. Marching Ahead: Imaging Biomarkers, a new revolution in patient management and care for Human Papilloma Virus (HPV) Positive Oropharyngeal Cancer. Rossy Cancer Network. CQI Research Fund. 25000 $ (2015-2016).

Grant. (Completed). Co-applicant. Texture Imaging: A novel technique to guide treatment and improve quality of life in patients with Non-Small Cell Lung Carcinoma (NSCLC). Rossy Cancer Network. CQI Research Fund. 25000 $ (2015-2016).

Publications

Articles de revue

  • Morin M, Vallières M, Braunstein S, Ginart JB, Upadhaya T, Woodruff HC, Zwanenburg A, Chatterjee A, Villanueva-Mayer JE, Valdes G, Chen W, Hong JC, Yom SS, Solberg TD, Löck S, Seuntjens J, Park C, and Lambin P. (2021). An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nature Cancer, 2(7), 709-722. (Published).
  • Decunha J, Poole CM, Vallières M, Torres J, Camilleri-Broët S, Rayes RF, Spicer JD, Enger SA. (2021). Development of patient specific 3D models from histopathological samples for microdosimetric investigations in radiation therapy. Physica Medica, 81, 162-169. (Published).
  • Chatterjee A, Vallières M, Forghani R, and Seuntjens J. (2021). Investigating the impact of the CT Hounsfield unit range on radiomic feature stability using dual energy CT data. Physica Medica, 88, 272-277. (Published).
  • Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Linh Tran TM, Choi JW, Wang D, Vallières M, Wang R, Collins S, Feng X, Feldman M, Zhang PJ, Atalay M, Sebro R, Yang L, Fan Y, Liao W-H, and Bai HX. (2021). Machine learning to predict COVID-19 severity and progression to critical illness using CT imaging and clinical data. Korean Journal of Radiology, 22(7), 1213-1224. (Published).
  • DeCunha J, Villegas F, Vallières M, Torres J, Camilleri-Broët S, and Enger S. (2021). Patient-specific microdosimetry: a proof of concept. Physics in Medicine and Biology, (Accepted).
  • Xi IL, Zhao Y, Wang R, Chang M, Purkayastha S, Chang K, Huang RY, Silva AC, Vallières M, Habibollahi P, Fan Y, Zou B, Gade TP, Zhang PJ, Soulen MC, Zhang Z, Bai HX, and Stavropoulos SW. (2020). Deep learning based on MR imaging for differentiation of low and high grade in low stage renal cell carcinoma. Journal of Magnetic Resonance Imaging, 52(5), 1542-1549. DOI. (Published).
  • Xi IL, Zhao Y, Wang R, Chang M, Purkayastha S, Chang K, Huang R, Silva A, Vallières M, Habibollahi P, Fan Y, Zou B, Gade T, Zhang P, Soulen M, Zhang Z, Bai H, and Stavropoulos S. (2020). Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clinical Cancer Research, 26(8), 1944-1952. DOI. (Published).
  • Bourbonne V, Fournier G, Vallières M, Lucia F, Doucet L, Tissot V, Cuvelier G, Hue S, Le Penn Du H, Perdriel L, Bertrand N, Staroz F, Visvikis D, Pradier O, Hatt M, Schick U. (2020). External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer. Cancers, 12(4), 814. DOI. (Published).
  • Zwanenburg A, Leger S, Vallières M, and Löck S. (2020). Image biomarker standardisation initiative. arXiv, arXiv:1612.07003, (Published).
  • Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen S, El Naqa I. (2020). Machine and deep learning methods for radiomics. Medical Physics, 47(5), e185-e202. (Published).
  • Chatterjee A, Vallières M, and Seuntjens J. (2020). Overlooked pitfalls in multi-class classification and how to avoid them. Physica Medica: European Journal of Medical Physics, 79, 96-100. DOI. (Published).
  • Depeursinge A, Andrearczyk V, Whybra P, van Griethuysen J, Müller H, Schaer R, Vallières M, and Zwanenburg A. (2020). Standardised convolutional filtering for radiomics. arXiv, arXiv:2006.05470, (Published).
  • Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit M-C, Dinapoli N, Viet Dinh C, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Guckenberger M, Götz, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegård A, Maier-Hein KH, Morin O, Müller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Fernandez JS, Spezi E, Steenbakkers RJHM, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C*, and Löck S*. (2020). The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2), 328-338. DOI. (Published).
  • Chatterjee A, Vallières M, Dohan A, Levesque IR, Ueno Y, Bist V, Saif S, Reinhold C, Seuntjens J. (2019). An empirical approach for avoiding false discoveries when applying high-dimensional radiomics to small datasets. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 201-209. DOI. (Published).
  • Wei L, Rosen B, Vallières M, Chotchutipan T, Mierzwa M, Eisbruch A, El Naqa I. (2019). Automatic recognition of streak artifacts in head and neck CT region of interest using gradient-based features and impact of streak artifacts for radiomic analysis. Physics and Imaging in Radiation Oncology, 10, 49-54. DOI. (Published).
  • Upadhaya T, Vallières M, Chatterjee A, Lucia F, Bonaffini PA, Masson I, Mervoyer A, Reinhold C, Schick U, Seuntjens J, Cheze Le Rest C, Visvikis D, Hatt M. (2019). Comparison of radiomics models built through machine learning in a multicentric context with independent testing: identical data, similar algorithms, different methodologies. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 192-200. DOI. (Published).
  • Chatterjee A, Vallières M, Dohan A, Levesque IR, Ueno Y, Bist V, Saif S, Reinhold C, Seuntjens J. (2019). Creating robust predictive radiomic models for data from independent institutions using normalization. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 210-215. DOI. (Published).
  • Diamant A, Chatterjee A, Vallières M, Shenouda G, Seuntjens J. (2019). Deep learning in head & neck cancer outcome prediction. Scientific Reports, 9, 2764. DOI. (Published).
  • Lucia F, Visvikis D, Vallières M, Desseroit M-C, Miranda O, Robin P, Bonaffini PA, Alfieri J, Masson I, Mervoyer A, Reinhold C, Pradier O, Hatt M, Schick U. (2019). External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemotherapy. European Journal of Nuclear Medicine and Molecular, 46(4), 864-867. DOI. (Published).
  • Morin O, Chen WC, Nassiri F, Susko M, Magill ST, Vasudevan HN, Wu A, Vallières M, Gennatas ED, Valdes G, Pekmezci M, Alcaide-Leon P, Choudhury A, Interian Y, Mortezavi S, Turgutlu K, Bush NAO, Solberg TD, Braunstein SE, Sneed PK, Perry A, Zadeh G, McDermott MW, Villanueva-Meyer JE, and Raleigh DR. (2019). Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival. Neuro-Oncology Advances, 1(1), 1-5. DOI. (Published).
  • Bourbonne V, Vallières M, Lucia F, Doucet L, Visvikis D, Tissot V, Pradier O, Hatt M, and Schick U. (2019). MRI-derived radiomics to guide post-operative management for high-risk prostate cancer. Frontiers in Oncology, 9, 807. DOI. (Published).
  • Nair JR, Vallières M, Mascarella M, Sabbagh EN, Duchatellier CF, Zeitouni A, Shenouda G, Chankowsky J. (2019). MRI texture analysis predicts recurrence in patients with nasopharyngeal carcinoma. Canadian Association of Radiologists, 70, 394-402. DOI. (Published).
  • Zhou H, Chang K, Bai HX, Xiao B, Su C, Bi WL, Zhang PJ, Senders JT, Vallières M, Kavouridis VK, Boaro A, Arnaout O, Yang L, Huang RY. (2019). Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas. Journal of Neuro-Oncology, 142(2), 299-307. DOI. (Published).
  • Ibrahim A, Vallières M, Woodruff H, Primakov S, Beheshti M, Keek S, Refaee T, Sanduleanu S, Walsh S, Morin O, Lambin P, Hustinx R, Mottaghy FM. (2019). Radiomics analysis for clinical decision support in nuclear medicine. Seminars in Nuclear Medicine, 49(5), 438-449. DOI. (Published).
  • Morin O, Vallières M, Jochems A, Woodruff HC, Valdes G, Braunstein SE, Wildberger JE, Villanueva-Meyer JE, Kearney V, Yom SS, Solberg TD, Lambin P. (2018). A deep look into the future of quantitative imaging in oncology: a statement of working principles and proposal for change. International Journal of Radiation Oncology • Biology • Physics, 102(4), 1074-1082. DOI. (Published).
  • Vallières M, Serban M, Benzyane I, Ahmed Z, Xing S, El Naqa I, Levesque IR, Seuntjens J, Freeman CR. (2018). Investigating the role of functional imaging in the management of soft-tissue sarcomas of the extremities. Physics and Imaging in Radiation Oncology, 6, 53-60. DOI. (Published).
  • Vallières M, Zwanenburg A, Badic B, Cheze Le Rest C, Visvikis D, Hatt M. (2018). Responsible radiomics research for faster clinical translation. Journal of Nuclear Medicine, 59(2), 189-193. DOI. (Published).
  • Vallières M, Laberge S, Diamant A, El Naqa I. (2017). Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept. Physics in Medicine and Biology, 62(22), 8536-8565. DOI. (Published).
  • Zhou H, Vallières M, Bai HX, Su C, Tang H, Oldridge D, Zhang Z, Xiao B, Liao W, Tao Y, Zhou J, Zhang P, Yang L. (2017). MR imaging features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro-Oncology, 19(6), 862-870. DOI. (Published).
  • Vallières M, Kay-Rivest E, Jean Perrin L, Liem X, Furstoss C, Aerts HJWL, Khaouam N, Nguyen-Tan PF, Wang C-S, Sultanem K, Seuntjens J, El Naqa I. (2017). Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Scientific Reports, 7, 10117. DOI. (Published).
  • Hatt M, Majdoub M, Vallières M, Tixier F, Cheze Le Rest C, GroheuxD, Hindié E, Martineau A, Pradier O, Hustinx R, Perdrisot R, Guillevin R, El Naqa I, Visvikis D. (2015). 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. Journal of Nuclear Medicine, 56(1), 38-44. DOI. (Published).
  • Vallières M, Freeman CR, Skamene SR, El Naqa I. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in Medicine and Biology, 60(14), 5471-5496. DOI. (Published).
  • Rivard M, Laliberté M, Bertrand-Grenier A, Harnagea C, Pfeffer CP, Vallières M, St-Pierre Y, Pignolet A, Khakani MAE, Légaré F. (2011). The structural origin of second harmonic generation in fascia. Biomedical Optics Express, 2(1), 26-36. DOI. (Published).
  • Harnagea C, Vallières M, Pfeffer CP, Wu D, Olsen BR, Pignolet A, Légaré F, Gruverman A. (2010). Two-dimensional nanoscale structural and functional imaging in individual collagen type-I fibrils. Biophysical Journal, 98(12), 3070-3077. DOI. (Published).
  • Patskovsky S, Vallières M, Maisonneuve M, Song I-H, Meunier M, Kabashin AV. (2009). Designing efficient zero calibration point for phase-sensitive surface plasmon resonance biosensing. Optics Express, 17(4), 2255-2263. DOI. (Published).

Chapitres de livre

  • Nano T, Lafrenière M, Ziemer B, Witztum A, Barrios J, Upadhaya T, Vallières M, Interian Y, Valdes G, Morin O. (2020). Artificial Intelligence in Radiation Oncology. Jacob Van Dyk The Modern Technology of Radiation Oncology (4, 225-258). United States : Medical Physics Publishing. (Published).

Rapports

  • Depeursinge A, Andrearczyk V, Whybra P, van Griethuysen J, Müller H, Schaer R, Vallières M, Zwanenburg A. (2020). Standardised convolutional filtering for radiomics. arXiv:2006.05470v1. 51 p.
  • Zwanenburg A, Leger S, Vallières M, Löck S. (2016). Image biomarker standardisation initiative. arXiv:1612.07003v11. 178 p.

Documents de travail

  • Depeursinge A, Andrearczyk V, Whybra P, van Griethuysen J, Müller H, Schaer R, Vallières M*, and Zwanenburg A*. (2020). Standardised convolutional filtering for radiomics. 54 p.
  • Zwanenburg A, Leger S, Vallières M, Löck S. (2016). Image biomarker standardisation initiative. https://arxiv.org/abs/1612.07003.

Articles de conférence

  • Andrearczyk V, Oreiller V, Jreige M, Vallières M, Castelli J, Elhalawani H, Boughdad S, Prior JO, Depeursinge A. (2021). Overview of the HECKTOR challenge at MICCAI 2020: automatic head and neck tumor segmentation in PET/CT. Lecture Notes in Computer Science, 1-21. (Published).
  • Andrearczyk V, Oreiller V, Vallières M, Castelli J, Elhalawani H, Boughdad S, Jreige M, Prior JO, and Depeursinge A. (2020). Automatic segmentation of head and neck tumors and nodal metastases in PET-CT scans. Proceedings of Machine Learning Research, 33-43. (Published).
  • Vallières M, Visvikis D, Hatt M. (2018). Dependency of a validated radiomics signature and potential corrections. Journal of Nuclear Medicine, 640. DOI. (Published).
  • Vallières M, Chatterjee A, Lucia F, Bourbonne V, Bonaffini P, Masson I, Mervoyers A, Reinhold C, Visvikis D, Schick U, Seuntjens J, Morin O, Hatt M. (2018). Investigating the complementarity of radiomics and clinical information for predicting treatment failure in multiple cancer types. Medical Physics, E679. DOI. (Published).
  • Vallières M, Boustead A, Laberge S, Levesque IR, El Naqa I. (2015). A machine learning approach for creating texture-preserved MRI tumor models from clinical sequences. Medical Physics, 3323-3324. DOI. (Published).
  • Vallières M, Freeman CR, Ahmed Z, Turcotte R, Hickeson M, Skamene S, Jeyaseelan K, Hathout L, Serban M, Xing S, Powell TI, Seuntjens J, Levesque IR, El Naqa I. (2015). Early assessment of tumor aggressiveness using joint FDG-PET/MRI textural features: prediction of prospective cohort and potential improvements using hypoxia and perfusion biomarkers. International Journal of Radiation Oncology Biology Physics, S6. DOI. (Published).
  • Vallières M, Freeman CR, Skamene S, El Naqa I. (2014). Early assessment of tumor aggressiveness using joint FDG-PET/MR textural features. International Journal of Radiation Oncology Biology Physics, S6-S7. DOI. (Published).
  • Vallières M, Laberge S, Levesque IR, El Naqa I. (2014). Enhancement of texture-based metastasis prediction models via the optimization of PET/MRI acquisition protocols. Medical Physics, 434-435. DOI. (Published).
  • Vallières M, Kumar A, Sultanem K, El Naqa I. (2013). FDG-PET Image-derived features can determine HPV status in head and neck cancer. International Journal of Radiation Oncology Biology Physics, S467. DOI. (Published).
  • Vallières M, Kumar A, Sultanem K, El Naqa I. (2013). FDG-PET imaging features can predict treatment outcomes in head and neck cancer. Medical Physics, 519. DOI. (Published).
  • Vallières M, Freeman C. Skamene S. El Naqa I. (2013). Joint FDG-PET/MR imaging for the early prediction of tumor outcomes. Medical Physics, 477. DOI. (Published).
  • Vallières M, Freeman CR, Skamene SR, El Naqa I. (2012). FDG-PET features and outcomes in soft-tissue sarcomas of the extremities. International Journal of Radiation Oncology Biology Physics, S167-S168. DOI. (Published).
  • Vallières M, Freeman CR, Skamene SR, El Naqa I. (2012). Prediction of tumor outcomes through wavelet image fusion and texture analysis of PET/MR imaging. Medical Physics, 3615. DOI. (Published).

Autres contributions

Gestion d'évènements

  • Program committee member. (2022). Summer school on deep learning for medical imaging. (Workshop).
  • Program committee member. (2020). World Molecular Imaging Congress (WMIC). (Conference).

Activités de collaboration internationale

  • Co-leader, MEDomics consortium. (2018-2025). United States. MEDomicsThe consortium (https://www.medomics.ai/) was officially launched in August 2018, and is currently composed of 12 scientists from six institutions: (i) Interdisciplinary Research Group in Health Informatics (GRIIS) of Université de Sherbrooke (UdeS), Canada; (ii) University California San Francisco (UCSF), USA; (iii) Princess Margaret Cancer Centre in Toronto, Canada; (iv) Department of Precision Medicine, Maastricht University, Netherlands; (v) Oncoray Research Group in Dresden, Germany; and (vi) CHU de Québec Research Centre, Québec, Canada. , and is currently expanding. Overall, the main motivation of this consortium is to develop an end-to-end, open-source computation platform for integrative data modeling in medicine: . We envision that will continue to produce high-impact scientific contributions to the field of.
  • Co-leader, Image Biomarker Standardisation Initiative (IBSI). (2016-2025). Germany. The image biomarker standardisation initiative (IBSI, https://theibsi.github.io/) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics). This initiative was launched by Alex Zwanenburg (Germany) and Martin Vallières (Sherbrooke) in September 2016. Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field. Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers. The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis.

Présentations

  • (2021). Development of a computation platform for integrative data modeling in oncology. Mila TechAIDE – AI conference. Montreal, Canada.
  • (2021). MEDomics: integrative data modeling in oncology. Medical image analysis and deep learning in Python (McMedHacks). Montreal, Canada.
  • (2021). Radiomics: the Image Biomarker Standardisation Initiative (IBSI). RMP Radiomics Symposium. Toronto, Canada.
  • (2020). MEDomicsLab: Integrative data modeling in oncology. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) / Session: Tools, Software, Data Commons and Architectures for Fusion of Imaging and non-Imaging Data. Lima, Peru.
  • (2020). Radiomics: the Image Biomarker Standardisation Initiative (IBSI). 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) / Session: 3D Head and Neck Tumor Segmentation in PET/CT. Lima, Peru.
  • (2019). Educational Lecture: "Radiomics: the Image Biomarker Standardisation Initiative (IBSI)". The International Conference on the Use of Computers in Radiation Therapy (ICCR 2019). Montreal, Canada.
  • (2019). Educational Lecture: "Radiomics: the Image Biomarker Standardisation Initiative (IBSI)". 3rd ESTRO Physics Workshop – Science in development. Session: Multi-source data fusion for decision support systems in radiation oncology: opportunities, methodologies, standardization and clinical translation. Budapest, Hungary.
  • (2019). Oral Presentation: "MEDomics: synergie entre analyse d’images médicales, apprentissage automatique, apprentissage profond, traitement automatique des langues naturelles et apprentissage distribué". Séminaire du département de médecine nucléaire et de radiobiologie du Centre Hospitalier Universitaire de Sherbrooke (CHUS). Sherbrooke, Canada.
  • (2019). Oral Presentation: "MEDomicsLab: an open-source computation platform for integrative data modeling in medicine". Practical Big Data Workshop 2019 (PBDW 2019). Ann Harbor, United States.
  • (2019). Oral Presentation: "Radiomics: the Image Biomarker Standardisation Initiative (IBSI)". Seminar of the Stanford Center for Biomedical Informatics Research. Palo Alto, United States.
  • (2018). Educational Lecture: "Introduction to convolutional neural networks (CNNs)". Big Data 4 Imaging 2018 Workshop. Maastricht, Netherlands.
  • (2018). Educational Lecture: "Radiomics: the Image Biomarker Standardisation Initiative (IBSI)". Big Data 4 Imaging 2018 Workshop. Masstricht, Netherlands.
  • (2018). Educational Lecture: "Radiomics in MRI: Getting started". Joint annual meeting ISMRM-ESMRMB 2018. Paris, France.
  • (2018). Oral Presentation: "Investigating the complementarity of radiomics and clinical information for predicting treatment failure in multiple cancer types". American Association of Physicists in Medicine (AAPM) 60th Annual Meeting. Nashville, United States.
  • (2017). Oral Presentation: "Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept". Congrès National d’Imagerie du Vivant (CNIV) 2017. Paris, France.
  • (2017). Oral Presentation: "IBSI: Current status and beyond". Radiomics Retreat 2017. Clearwater, FL, United States.
  • (2017). Oral Presentation: "Radiomics: Enabling Factors Towards Precision Medicine". RMP Radiomics Symposium Princess Magaret Hospital (PMH). Toronto, Canada.
  • (2016). Oral Presentation: "Analyse texturale pour l’évaluation de l’agressivité des tumeurs". Séminaire du département de radiothérapie du Centre hospitalier de l’Université de Montréal (CHUM). Montréal, Canada.
  • (2016). Oral Presentation: "Assessing the risk of tumour recurrences and metastases in head and neck cancer by combining radiomics and clinical variables via imbalance-adjusted machine learning". Radiomics Retreat 2016. Clearwater, FL, United States.
  • (2015). Oral Presentation: "Radiomics: Mais Ou Et Donc Car Ni Or (who, what, when, where, when)?". Medical Physics Research Training Network (MPRTN) CREATE: Rising Stars in Medical Physics. Montréal, Canada.
  • (2015). Oral Presentation: "Statistical methods for the construction of texture-based prediction models". Radiomics Retreat 2015. Clearwater, FL, United States.
  • (2012). Oral Presentation: "PET/MR imaging for prediction of tumor outcomes by wavelet image fusion and texture analysis". PET/MR and SPECT/MR: New Paradigms for Combined Modalities in Molecular Imaging Conference (PSMR2012). La Biodola, Elba Island, Italy.
  • (2012). Oral Presentation: "Prediction of tumour outcomes by wavelet image fusion and texture analysis". Seminar of the Montreal Neurological Institute (MNI). Montréal, Canada.