Mila > Team > Martin Vallières

Martin Vallières

Associate Academic Member
Associate Academic Member
Assistant Professor, Université de Sherbrooke, Canada CIFAR AI Chair

Martin Vallières is Assistant Professor in the Department of Computer Science of Université de Sherbrooke (April 2020). He received a PhD in Medical Physics from McGill University in 2017, and completed post-doctoral training in France and USA in 2018 and 2019. The overarching goal of Martin Vallières’ research is centered on the development of clinically-actionable models to better personalize cancer treatments and care (“precision oncology”). He is an expert in the field of radiomics (i.e. the high-throughput and quantitative analysis of medical images) and machine learning in oncology. Over the course of his career, he has developed multiple prediction models for different types of cancers. His main research interest is now focused on the graph-based integration of heterogeneous medical data types for improved precision oncology.



Patient-specific microdosimetry: a proof of concept.
Joseph M DeCunha, Fernanda Villegas, Martin Vallières, Jose Torres, Sophie Camilleri-Broët and Shirin A Enger
Physics in Medicine and Biology
Investigating the impact of the CT Hounsfield unit range on radiomic feature stability using dual energy CT data.
Avishek Chatterjee, Martin Valliéres, Reza Forghani and Jan Seuntjens
Physica Medica


An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication
Olivier Morin, Martin Vallières, Steve Braunstein, Jorge Barrios Ginart, Taman Upadhaya, Henry C. Woodruff, Alex Zwanenburg, Avishek Chatterjee, Javier E. Villanueva-Meyer, Gilmer Valdes, William Chen, Julian C. Hong, Sue S. Yom, Timothy D. Solberg, Steffen Löck, Jan Seuntjens, Catherine Park and Philippe Lambin


Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.
Subhanik Purkayastha, Yanhe Xiao, Zhicheng Jiao, Rujapa Thepumnoeysuk, Kasey Halsey, Jing Wu, Thi My Linh Tran, Ben Hsieh, Ji Whae Choi, Dongcui Wang, Martin Vallières, Robin Wang, Scott Collins, Xue Feng, Michael Feldman, Paul J Zhang, Michael Atalay, Ronnie Sebro, Li Yang, Yong Fan... (2 more)
Korean Journal of Radiology
HEad and neCK TumOR segmentation and outcome prediction in PET/CT images
Vincent Andrearczyk, Valentin Oreiller, Martin Vallières, Mathieu Hatt, Catherine Cheze-Le Rest, Dimitris Visvikis, Mario Jreige, Hesham Elhalawani, Sarah Boughdad, John O. Prior and Adrien Depeursinge
(venue unknown)


FDG-PET/CT Radiomics Models for The Early Prediction of Locoregional Recurrence in Head and Neck Cancer
Hu Cong, Wang Peng, Zhou Tian, Martin Vallières, Xu Chuanpei, Zhu Aijun and Zhang Benxin
Current Medical Imaging Formerly Current Medical Imaging Reviews


Development of patient-specific 3D models from histopathological samples for applications in radiation therapy
Joseph M. DeCunha, Christopher M. Poole, Martin Vallières, Jose Torres, Sophie Camilleri-Broët, Roni F. Rayes, Jonathan D. Spicer and Shirin A. Enger
Physica Medica


PO-1531: Publishing linked and FAIR radiomics data in radiation oncology via ontologies and Semantic Web
A. Traverso, M. Vallieres, J. Van Soest, L. Wee, O. Morin and A. Dekker
Radiotherapy and Oncology


Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT
Vincent Andrearczyk, Valentin Oreiller, Mario Jreige, Martin Vallières, Joel Castelli, Hesham Elhalawani, Sarah Boughdad, John O. Prior and Adrien Depeursinge
3D Head and Neck Tumor Segmentation in PET/CT Challenge


Standardised convolutional filtering for radiomics.
Adrien Depeursinge, Vincent Andrearczyk, Philip Whybra, Joost van Griethuysen, Henning Müller, Roger Schaer, Martin Vallières and Alex Zwanenburg
arXiv preprint arXiv:2006.05470


The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah, Hugo J. W. L. Aerts, Vincent Andrearczyk, Aditya Apte, Saeed Ashrafinia, Spyridon Bakas, Roelof J. Beukinga, Ronald Boellaard, Marta Bogowicz, Luca Boldrini, Irène Buvat, Gary J. R. Cook, Christos Davatzikos, Adrien Depeursinge, Marie-Charlotte Desseroit, Nicola Dinapoli, Cuong Viet Dinh, Sebastian Echegaray... (43 more)
Machine and deep learning methods for radiomics.
Michele Avanzo, Lise Wei, Joseph Stancanello, Martin Vallières, Arvind Rao, Olivier Morin, Sarah A. Mattonen and Issam El Naqa
Medical Physics


Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging.
Ianto Lin Xi, Yijun Zhao, Robin Wang, Marcello Chang, Subhanik Purkayastha, Ken Chang, Raymond Y. Huang, Alvin C. Silva, Martin Valliéres, Peiman Habibollahi, Yong Fan, Beiji Zou, Terence P. Gade, Paul J. Zhang, Michael C. Soulen, Zishu Zhang, Harrison X. Bai and S. William Stavropoulos
Clinical Cancer Research

Publications collected and formatted using Paperoni