Martin Vallières

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Associate Academic Member
Martin Vallières
Assistant Professor, Université de Sherbrooke
Martin Vallières

Martin Vallières is a newly appointed 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.

Publications

2021-01

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
(venue unknown)
(2021-01-01)
link.springer.com
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
(2021-01-01)
www.eurekaselect.com

2020-11

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
(2020-11-01)
api.elsevier.com

2020-06

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
(2020-06-09)
dblp.uni-trier.dePDF

2020-05

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
(2020-05-01)
aapm.onlinelibrary.wiley.com

2020-04

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
(2020-04-15)
clincancerres.aacrjournals.orgPDF

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