Martin Carrier-Vallières

Prix et distinctions

• 1st prize: Rising Star in Medical Physics Symposium 2015. Medical Physics Research Training Network (MPRTN). (Prize / Award).
• Cum Laude award - Education Exhibit 2021. Radiological Society of North America. (Prize / Award).
• Michael S. Patterson Publication Impact Prize in Medical Physics 2021. Canadian Organization of Medical Physicists. (Prize / Award).
• Researcher of the month. Centre Hospitalier Univ. de Sherbrooke. (Distinction).
• 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. 132 500 $. (2021-2026)
• Grant. (Awarded). Principal Investigator. L’intelligence artificielle en oncologie. Institut de recherche sur le cancer de l'Université de Sherbrooke (IRCUS). Souper-brainstorming de l’IRCUS 2022. 25 000 $. (2023-2025)
• 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. 500 000 $. (2020-2025)
• Grant. (Awarded). Principal Investigator. Apprentissage en continu pour l’optimisation des trajectoires de soins en chimiothérapie. University of Sherbrooke. Programme d’appel à projets pour la recherche interdisciplinaire et interfacultaire. 10 000 $. (2023-2024)
• Grant. (Awarded). Principal Investigator. MEDomicsLab : modélisation intégrative de données hétérogènes en médecine. Unité de soutien SSA Québec. Axe Gestion des données. 160 000 $. (2023-2024)
• Grant. (Awarded). Principal Investigator. Multi-level graphical modeling of healthcare data for developing mortality risk models. Fonds de recherche du Québec - Nature et technologies (FRQNT). Établissement de la relève professorale. 80 000 $. (2022-2024)
• Grant. (Awarded). Co-investigator. Quels sont les droits de l’usager de la trajectoire de cancérologie au Québec. Institut de recherche sur le cancer de l'Université de Sherbrooke (IRCUS). Souper-brainstorming de l’IRCUS 2023. 25 000 $. (2023-2024)
• Grant. (Awarded). Co-investigator. L’application de la génomique en oncologie. Institut de recherche sur le cancer de l'Université de Sherbrooke (IRCUS). Souper-brainstorming de l’IRCUS 2022. 25 000 $. (2022-2023)
• Grant. (Completed). Principal Investigator. Development of Artificial Intelligence Techniques for Automated Electric Power Asset Identification. Ministère de l’Économie et de l’Innovation (MEI) du Québec. InnovÉÉ - Innovation en énergie électrique. 66 200 $. (2021-2022)
• Grant. (Completed). 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). 66 200 $. (2021-2022)
• Grant. (Completed). Co-investigator. Financement en IA – Volet Propulsion des universités. PROMPT-Québec. PROMPT-IA – Volet propulsion des universités. 500 000 $. (2020-2022)
• Grant. (Completed). Principal Investigator. Start-up funds, Faculty of Sciences. University of Sherbrooke. Start-up funds. 30 000 $. (2020-2022)
• Grant. (Completed). 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. 40 000 $. (2020-2022)
• Grant. (Completed). Principal Investigator. Energ-AI: Artificial Intelligence in Electrical Power Engineering. Mathematics of Information Technology and Complex Systems (MITACS). Mitacs Accelerate. 15 000 $. (2020-2020)

Publications

Articles de revue

• Andrearczyk V, Oreiller V, Boughdad S, Cheze Le Rest C, Tankyevych O, Elhalawani H, Jreige M, Prior JO, Vallières M, Visvikis D, Hatt M, and Depeursinge A. (2023). Automatic head and neck tumor segmentation and outcome prediction relying on FDG-PET/CT images: findings from the second edition of the HECKTOR challenge. Medical Image Analysis 90 102972. (Published).
• Giard-Leroux S, Cléroux G, Kulkarni SS, Bouffard F, and Vallières M. (2023). Electric Power Fuse Identification with Deep Learning. IEEE Transactions on Industrial Informatics 19 (11), 11310-11321. (Published).
• Whybra P, Zwanenburg A, Andrearczyk V, Schaer R, Apte A, Ayotte A, Baheti B, Bakas S, Bettinelli A, Boellaard R, Boldrini L, Buvat I, Cook G, Dietsche D, Dinapoli N, Gabrys H, Goh V, Guckenberger M, Hatt M, Hosseinzadeh M, Iyer A, Lenkowicz J, Loutfi M, Löck S, Marturano F, Morin O, Nioche C, Orlhac F, Pati S, Rahmim A, Rezaeijo SM, Rookyard C, Salmanpour M, Schindele A, Shiri I, Spezi E, Tanadini-Lang S, Tixier F, Upadhaya T, Valentini V, van Griethuysen J, Yousefirizi F, Zaidi H, Müller H, Vallières M, and Depeursinge A. (2023). The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Radiology (In Press).
• Fontaine P, Andrearczyk V, Oreiller V, Abler D, Castelli J, Acosta O, De Crevoisier R, Vallières M, Jreige M, Prior JO, and Depeursinge A. (2022). Cleaning Radiotherapy Contours for Radiomics Studies, is it Worth it? A Head and Neck Cancer Study. Clinical and Translational Radiation Oncology 33 153-158. (Published).
• Lefebvre TL, Ueno Y, Dohan A, Chatterjee A, Vallières M, Winter-Reinhold E, Saif S, Levesque IR, Zeng XZ, Forghani R, Seuntjens J, Soyer P, Savadjiev P, and Reinhold C. (2022). Development and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer. Radiology N/A 212873. (Published).
• Pati S, Baid U, Edwards B, [275 authors], and Bakas S. (2022). Federated Learning Enables Big Data for Rare Cancer Boundary Detection. Nature Communications 13 7346. (Published).
• Oreiller V, Andrearczyk V, Jreige M, Boughdad S, Elhalawani H, Castelli J, Vallières M, Zhu S, Xie J, Peng Y, Iantsen A, Hatt M, Yuan Y, Ma J, Yang X, Rao C, Pai S, Ghimire K, Feng X, Naser MA, Fuller CD, Yousefirizi F, Rahmim A, Chen H, Wang L, Prior JO, and Depeursinge A. (2022). Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge. Medical Image Analysis 77 102336. (Published).
• Keek SA, Beuque M, Primakov S, Woodruff HC, Chatterjee A, van Timmeren JE, Vallières M, Hendriks LEL, Kraft J, Andratschke N, Braunstein SE, Morin O, and Lambin P. (2022). Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics. Frontiers in Oncology 12 920393. (Published).
• George E, Flagg E, Chang K, Bai HX, Aerts HJ, Vallières M, Reardon DA, and Huang RY. (2022). Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma. American Journal of Neuroradiology 43 (5), 675-681. (Published).
• 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).
• Cong H, Peng W, Tian Z, Vallières M, Chuanpei X, Aijun Z, and Benxin Z. (2021). FDG-PET/CT Radiomics Models for The Early Prediction of Locoregional Recurrence in Head and Neck Cancer. Current Medical Imaging 17 (3), 374-383. (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-based prediction of COVID-19 severity and progression to critical illness using CT imaging and clinical data. Korean Journal of Radiology 22 (7), 1213-1224. (Published).
• Ginart JB, Ziemer BP, Nano T, Turgutlu KC, Ibrahim A, Interian Y, Dalal A, Sandor R, Leseur J, Vallières M, Upadhaya T, Braunstein S, Valdes G, McDermott M, Villanueva-Meyer J, and Morin O. (2021). Multi-Modal Brain and Ventricle Segmentation Using Weakly Supervised Transfer Learning. J Radiol Med Imaging 4 (1), 1052. (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 66 (18), 185011. (Published).
• 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. (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. (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. (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. (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. (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. (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. (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. (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. (Published).
• Diamant A, Chatterjee A, Vallières M, Shenouda G, Seuntjens J. (2019). Deep learning in head & neck cancer outcome prediction. Scientific Reports 9 2764. (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. (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. (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. (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. (Published).
• Nair JKR, Vallières M, Mascarella MA, El Sabbagh N, Duchatellier CF, Zeitouni A, Shenouda G, and Chankowsky J. (2019). Magnetic resonance imaging texture analysis predicts recurrence in patients with nasopharyngeal carcinoma. Canadian Association of Radiologists Journal 70 (4), 394-402. (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. (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. (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. (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. (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. (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. (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. (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. (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. (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. (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. (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. (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 of America : Medical Physics Publishing. (Published).

Documents de travail

• Pati S, Baid U, Edwards B, [275 authors], and Bakas S. (2022). Federated Learning Enables Big Data for Rare Cancer Boundary Detection. 21 p.
• Raymond N, Caru M, Laribi H, Mitiche M, Marcil V, Krajinovic M, Curnier D, Sinnett D, and Vallières M. (2022). Machine learning strategies to predict late adverse effects in childhood acute lymphoblastic leukemia survivors. 32 p.
• 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. 178 p.

Articles de conférence

• Andrearczyk V, Oreiller V, Abobakr M, Akhavanallaf A, Balermpas P, Boughdad S, Capriotti L, Castelli J, Cheze Le Rest C, Decazes P, Correia R, El-Habashy D, Elhalawani H, Fuller CD, Jreige M, Khamis Y, La Greca A, Mohamed A, Naser M, Prior JO, Ruan S, Tanadini-Lang S, Tankyevych O, Salimi Y, Vallières M, Vera P, Visvikis D, Wahid K, Zaidi H, Hatt M, and Depeursinge A. (2023). Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images. Lecture Notes in Computer Science, 1-30. (Published).
• Larose M, Touma N, Raymond N, LeBlanc D, Rasekh F, Neveu B, Hovington H, Vallières M, Pouliot P, and Archambault L. (2022). Graph Attention Network for Prostate Cancer Lymph Node Invasion Prediction. Medical Imaging with Deep Learning. (Published).
• Andrearczyk V, Oreiller V, Boughdad S, Cheze Le Rest C, Elhalawani H, Jreige M, Prior JO, Vallières M, Visvikis D, Hatt M, and Depeursinge A. (2022). Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images. Lecture Notes in Computer Science, 1-37. (Published).
• 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).
• Chatterjee A, Vallières M, Seuntjens J, and Forghani R. (2020). Advantages of Spectral Energy CT Data for Deep Learning Applications. Medical Physics, E575-E575. (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).
• Traverso A, Vallières M, Van Soest J, Wee L, Morin O, and Dekker A. (2020). Publishing linked and FAIR radiomics data in radiation oncology via ontologies and Semantic Web. Radiotherapy and Oncology, S827-S827. (Published).
• Ferreira MDS, LOVINFOSSE P, DE CUYPERE M, Rovira R, Lucia F, Schick U, Vallières M, Bonaffini P, Reinhold C, Visvikis D, Hatt M, BERNARD C, Leijenaar R, Walsh S, KRIDELKA F, Meyer P, and HUSTINX R. (2019). FDG PET radiomics to predict disease free survival in Cervical Cancer. IEEE Nuclear Science Symposium & Medical Imaging Conference. (Published).
• Da-ano R, Lucia F, Vallières M, Bonaffini P, Masson I, Mervoyer A, Reinhold C, Schick U, Visvikis D, and Hatt M. (2019). Harmonization strategies based on ComBat for mutlicentric radiomics studies. European Journal of Nuclear Medicine and Molecular Imaging, S254-S254. (Published).
• Chatterjee A, Vallières M, Dohan A, Levesque IR, Ueno Y, Saif S, Reinhold C, and Seuntjens J. (2019). Improved external validation performance of predictive radiomics models using statistical methods. Radiotherapy and Oncology, S513-S513. (Published).
• Bourbonne V, Vallières M, Lucia F, Fournier G, Valéri A, Visvikis D, Tissot V, Pradier O, Hatt M, and Schick U. (2019). MRI-derived radiomics to select patients with high-risk prostate cancer for adjuvant radiotherapy. Radiotherapy and Oncology, S451-S452. (Published).
• Diamant A, Chatterjee A, Vallières M, Shenouda G, and Seuntjens J. (2019). Multi-Branch Convolutional Neural Network Combines Unregistered PET and CT Images for Head & Neck Cancer Outcome Prediction. Medical Physics, E294-E294. (Published).
• Chatterjee A, Vallières M, Romero-Sanchez G, Perez-Lara A, Forghani R, and Seuntjens J. (2019). Multi-Energy Study of Impact of CT Hounsfield Unit Range in Gray Level Discretization On Radiomic Feature Stability. Medical Physics, E233-E233. (Published).
• Diamant A, Chatterjee A, Vallières M, Shenouda G, and Seuntjens J. (2019). Multi-modal deep learning framework for head & neck cancer outcome prediction. Medical Physics, 5372-5372. (Published).
• Traverso A, Vallières M, van Soest J, Wee L, Morin O, and Dekker A. (2019). Publishing Linked and FAIR-compliant Radiomics Data in Radiation Oncology via Ontologies and Semantic Web Techniques. Semantic Web Applications and Tools for Healthcare and Life Sciences. (Published).
• Ferreira MDS, LOVINFOSSE P, DE CUYPERE M, Rovira R, Lucia F, Schick U, Vallières M, Bonaffini P, Reinhold C, Visvikis D, Hatt M, BERNARD C, Leijenaar R, Walsh S, KRIDELKA F, Meyer P, and HUSTINX R. (2019). Radiomics for Disease Free Survival prediction using pre-treatment FDG PET images. Imaging of diagnostic and therapeutic biomarkers in Oncology. (Published).
• Chatterjee A, Vallières M, Dohan A, Levesque I, Ueno Y, Saif S, Reinhold C, and Seuntjens J. (2019). Using Dataset-Specific Feature Standardization to Improve Predictive Performance of Radiomic Models. Medical Physics, E174-E174. (Published).
• Lucia F, Visvikis D, Vallières M, Desseroit M, Miranda O, Robin P, Bonaffini PA, Alfieri J, Masson I, Mervoyer A, Reinhold C, Pradier O, Hatt M, and Schick U. (2019). Validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer. Radiotherapy and Oncology, S800. (Published).
• Bourbonne V, Vallières M, Lucia F, Doucet L, Visvikis D, Tissot V, Cuvelier G, Hue S, Prigent L, Bertrand N, Staroz F, Pradier O, Hatt M, and Schick U. (2019). Validation of an MRI-derived radiomics model to guide patients selection for adjuvant radiotherapy after prostatectomy for high-risk prostate cancer. International Journal of Radiation Oncology, Biology, Physics, E266-E267. (Published).
• Vallières M, Visvikis D, Hatt M. (2018). Dependency of a validated radiomics signature and potential corrections. Journal of Nuclear Medicine, 640. (Published).
• Hatt M, Vallières M, Visvikis D, and Zwanenburg A. (2018). IBSI: an international community radiomics standardization initiative. Journal of Nuclear Medicine, 287-287. (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, and Hatt M. (2018). Investigating the Complementarity of Radiomics and Clinical Information for Predicting Treatment Failure in Multiple Cancer Types. Medical Physics, E679-E679. (Published).
• Zwanenburg A, Abdalah A, Ashrafinia S, Beukinga J, Bogowicz M, Dinh CV, Götz M, Hatt M, Leijenaar R, Lenkowicz J, Morin O, Rao A, Fernandez JS, Vallières M, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Troost ECG, Richter C, and Löck S. (2018). Results from the image biomarker standardisation initiative. Radiotherapy and Oncology, (Published).
• Bourbonne V, Vallières M, Lucia F, Fournier G, Valéri A, Visvikis D, Pradier O, and Schick U. (2018). Valeur pronostique des paramètres de texture extraits des IRM préthérapeutiques chez les patients opérés d’un adénocarcinome prostatique à haut risque de récidive biochimique. Cancer/Radiothérapie, 695-696. (Published).
• Chatterjee A, Vallières M, Dohan A, Levesque I, Ueno Y, Bist V, Saif S, Reinhold C, and Seuntjens J. (2017). Keys to Avoiding Statistical Pitfalls of Small Datasets in Radiomics. Medical Physics, 3114-3114. (Published).
• Chatterjee A, Vallières M, Dohan A, Levesque I, Ueno Y, Bist V, Saif S, Reinhold C, and Seuntjens J. (2017). Novel methodology for applying radiomics to small datasets. Medical Physics, 4371-4371. (Published).
• Ybarra N, Vallières M, Jeyaseelan K, Freeman CR, Jung S, Turcotte R, Seuntjens J, and El Naqa I. (2016). Correlation of Molecular Imaging and Biomarkers Expression in the Prediction of Metastatic Capacity of Soft Tissue Sarcomas. International Journal of Radiation Oncology, Biology, Physics, E705-E706. (Published).
• Vallières M, Freeman CR, Zaki A, Turcotte R, Hickeson M, Skamene S, Jeyaseelan K, Hathout L, Serban M, Xing S, Powell TI, Goulding K, Seuntjens S, Levesque IR, and El Naqa I. (2016). EARLY ASSESSMENT OF LUNG METASTASIS RISK IN SOFT-TISSUE SARCOMAS: PREDICTION OF PROSPECTIVE COHORT AND POTENTIAL IMPROVEMENT USING HYPOXIA AND PERFUSION BIOMARKERS. Orthopaedic Proceedings, 39-39. (Published).
• Zhou H, Bai HX, Su C, Tang H, Vallières M, Huang X, Agbodza E, Awachie T, Tang X, Tao Y, Zhou J, Martinez-Lage M, Xiao B, Tan L, Zhang P, and Yang L. (2016). MR Imaging Features Predict Survival and Molecular Profile in Diffuse Lower Grade Gliomas. Annals of Neurology, S56-S56. (Published).
• Vallières M, Boustead A, Laberge S, Levesque IR, and El Naqa I. (2015). A Machine Learning Approach for Creating Texture-Preserved MRI Tumor Models From Clinical Sequences. Medical Physics, 3323-3324. (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, and El Naqa I. (2015). Early Assessment of Tumor Aggressiveness Using Joint FDG-PET/MRI Textural Features: Prediction of Prospective Cohort and Potential Improvement Using Hypoxia and Perfusion Biomarkers. International Journal of Radiation Oncology Biology Physics, S6. (Published).
• Lee S, Ybarra N, Jeyaseelan K, Faria S, Kopek N, Vallières M, and El Naqa I. (2014). Association of Computed Tomography image textures with inflammatory biomarkers in radiation-induced lung injury. Radiotherapy and Oncology, S28. (Published).
• Vallières M, Freeman CR, Skamene S, and 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. (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. (Published).
• Vallières M, Laberge S, Levesque IR, and El Naqa I. (2014). Enhancement of Texture‐Based Metastasis Prediction Models Via the Optimization of PET/MRI Acquisition Protocols. Medical Physics, 435-435. (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. (Published).
• Perez JR, Vallières M, Ybarra N, Maria O, Chagnon F, Lesur O, and El Naqa I. (2014). Fluorescence Endomicroscopy as a Tool to Assess Radiation-Induced Lung Damage, Protection, and Regeneration. International Journal of Radiation Oncology, Biology, Physics, S78. (Published).
• Pater P, Vallières M, and Seuntjens J. (2014). Hands-On Monte Carlo Project Assignment as a Method to Teach Radiation Physics. Medical Physics, 426-427. (Published).
• Laberge S, Vallières M, Levesque IR,and El Naqa I. (2014). STAMP: Simulator for Texture Analysis in MRI/PET. Medical Physics, 122-122. (Published).
• Vallières M, Kumar A, Sultanem K, and 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. (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. (Published).
• Vallières M, Kumar A, Sultanem K, and El Naqa I. (2013). FDG-PET Imaging Features Can Predict Treatment Outcomes in Head and Neck Cancer. Medical Physics, 519-519. (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. (Published).
• Vallières M, Freeman C, Skamene S, and El Naqa I. (2013). Joint FDG-PET/MR Imaging for the Early Prediction of Tumor Outcomes. Medical Physics, 477-477. (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. (Published).
• Markel D, El Naqa I, Freeman C, and Vallières M. (2012). A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the Jensen-Rényi Divergence. Medical Physics, 3678-3678. (Published).
• Markel D, El Naqa I, Freeman C, and Vallières M. (2012). A Novel Semi-automated Multimodality Segmentation Tool for Radiation Therapy Treatment Planning in Sarcoma Patients. International Journal of Radiation Oncology, Biology, Physics, S854. (Published).
• Seuntjens J, Serban M, Vallières M, Hathout L, Freeman C, and El Naqa I. (2012). Dose-escalation based on MR-PET/CT for soft-tissue sarcoma. International Journal of Radiation Oncology, Biology, Physics, S660-S661. (Published).
• Vallières M, Freeman CR, Skamene SR, El Naqa I. (2012). FDG-PET Features and Outcomes in Patients With Soft-tissue Sarcomas of the Extremities. International Journal of Radiation Oncology, Biology, Physics, S167-S168. (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. (Published).
• Vallières M, Freeman CR, Skamene SR, and El Naqa I. (2012). Prediction of Tumor Outcomes Through Wavelet Image Fusion and Texture Analysis of PET/MR Imaging. Medical Physics, 3615-3615. (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. (Published).

Autres contributions

Cours enseignés

• Techniques d'apprentissage. IFT603/712. (2023-09-01 à 2023-12-31).(3CR).
• Extraction de caractéristiques d’images médicales. IMN714. (2023-01-01 à 2023-04-30).(3CR).
• Techniques d'apprentissage. IFT603/712. (2022-09-01 à 2022-12-31).(3CR).
• Analyse d'images. IMN259. (2022-01-01 à 2022-04-30).(3CR).
• Techniques d'apprentissage. IFT603/712. (2022-01-01 à 2022-04-30).(3CR).
• Analyse d'images. IMN259. (2021-01-01 à 2021-04-30).(3CR).
• Programmation scientifique en Python. IFT211. (2021-01-01 à 2021-04-30).(1CR).
• Techniques d'apprentissage. IFT603/712. (2020-09-01 à 2020-12-31).(3CR).

Gestion d'évènements

• Committee member. (2023) Journée des études supérieures du département d'informatique de l'Université de Sherbrooke. (Seminar).
• Program committee member. (2020) World Molecular Imaging Congress (WMIC). (Conference).

Activités de collaboration internationale

• Co-leader, MEDomics consortium. United States of America. 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). 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

• (2023). An academic journey from Medical Physics to AI4Health: from diving to surfing research, and back again. Friday Seminar Series, Medical Physics Unit, McGill University. Montreal, Canada
• (2023). Apprentissage en continu pour l’optimisation des trajectoires de soins en chimiothérapie au CIUSSS de l’Estrie-CHUS. Symposium de la recherche sur le cancer Sherbrooke 2023. Sherbrooke, Canada
• (2023). Integrative modeling of heterogeneous data for better precision medicine. DELPHI Day - Lorsque les données de santé rencontrent les algorithmes. Nantes, France
• (2023). L'intelligence artificielle en oncologie. L’Institut de recherche sur le cancer de l’Université de Sherbrooke (IRCUS) ouvre ses portes à la Rose des vents de l’Estrie. Sherbrooke, Canada
• (2023). Le Réseau santé numérique (RSN) du Fonds de recherche du Québec - Santé (FRQS). Salon CITY HEALTHCARE. Nantes, France
• (2023). L’IA en santé et des pistes de contributions possibles pour des chercheurs académiques. NSERC CREATE - Responsible Health and Healthcare Data Science. Québec, Canada
• (2023). L’apprentissage fédéré pour la segmentation automatique des glioblastomes. BistroBrain - Neurosciences et intelligence artificielle. Sherbrooke, Canada
• (2022). L'intelligence artificielle dans la lutte contre le cancer. Les grandes découvertes de l'UdeS - BistroBrain. Sherbrooke, Canada
• (2022). MEDomics : synergie entre analyse d’images médicales, apprentissage automatique, apprentissage profond, traitement automatique du langage et apprentissage fédéré. Midi-conférences DMIG, Université du Québec à Rimouski (UQAR). Canada
• (2022). MEDomicsLab: Integrative data modeling in oncology. Event: International Conference on Radiation Medicine. Session: Artificial Intelligence in Radiation Medicine. Riyadh, Saudi Arabia
• (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). Montréal, Canada
• (2021). MEDomics: synergie entre analyse d’images médicales, apprentissage automatique, apprentissage profond, traitement automatique des langues naturelles et apprentissage distribué. Conférence Denis-LeBel et Kaféfak de la Faculté de sciences de l'Université de Sherbrooke. Sherbrooke, Canada
• (2021). MEDomics: synergie entre analyse d’images médicales, apprentissage automatique, apprentissage profond, traitement automatique des langues naturelles et apprentissage distribué. Séminaire de l'Université de Sherbrooke - Thème fédérateur Santé: Promotion, prévention et approches de précision. Sherbrooke, Canada
• (2021). Radiomics: the Image Biomarker Standardisation Initiative (IBSI). RMP Radiomics Symposium. Toronto, Canada
• (2021). Radiomics Analysis Using The Image Biomarker Standardization Initiative (IBSI): Benchmarks And Guidelines. 107th Scientific Assembly and Annual Meeting of the Radiological Society of North America. Chicago, United States of America
• (2020). MEDomics : synergie entre analyse d’images médicales, apprentissage automatique, apprentissage profond, traitement automatique du langage et apprentissage distribué. Séminaire du Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CRCHUS). Sherbrooke, 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). 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 of America
• (2019). Oral Presentation: "Radiomics: the Image Biomarker Standardisation Initiative (IBSI)". Seminar of the Stanford Center for Biomedical Informatics Research. Palo Alto, United States of America
• (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 of America
• (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 of America
• (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 of America
• (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 of America
• (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