Publications

Note: The publications presented here are those specific to the work of the MEDomicsLab laboratory. The list of publications, presentations and activities of Martin Vallières can be found in his Google Scholar and CV.

Journal Papers

• H. Laribi , M. Vallières (2025), Leveraging patients’ longitudinal data to improve the Hospital One-year Mortality Risk. In Health Information Science and Systems.

• M. Larose , N. Raymond , M. Vallières (2024), Multi-task Bayesian Model Combining FDG-PET/CT Imaging and Clinical Data for Interpretable High-Grade Prostate Cancer Prognosis. In Nature Scientific Reports.

• S. Giard-Leroux , G. Cléroux , S. Sunil Kulkarni , M. Vallières (2023), Electric Power Fuse Identification With Deep Learning. In IEEE Transactions on Industrial Informatics.

Conference Papers

• N. Raymond , H. Laribi , M. Mitiche , M. Vallières (2024), Development of Error Passing Network for Optimizing the Prediction of VO2 peak in Childhood Acute Leukemia Survivors. In Proceedings of Machine Learning Research.

• M. Larose , N. Raymond , M. Vallières (2022), Graph Attention Network for Prostate Cancer Lymph Node Invasion Prediction. In Quebec Bioimaging Network 2022.

Preprints

• H. Laribi , N. Raymond , M. Vallières (2024), Improving Mortality Prediction with Longitudinal Data. In Research Square.

• H. Laribi , M. Vallières (2024), Leveraging patients’ longitudinal data to improve the Hospital One-year Mortality Risk. In arXiv.

• M. Ait Lhaj Loutfi , T. Boblea Podasca , M. Vallières (2024), Strategies for Optimal Simplicity in Predictive Modeling. In arXiv.

• M. Larose , M. Vallières (2024), Multi-task Bayesian Model Combining FDG-PET/CT Imaging and Clinical Data for Interpretable High-Grade Prostate Cancer Prognosis. In medRxiv.

• N. Raymond , M. Vallières (2022), Machine learning strategies to predict late adverse effects in childhood acute lymphoblastic leukemia survivors. In arXiv.

Presentations

• O. Lefebvre , M. Vallières (2024), Identifying and Addressing Uncertainty in Medical Predictions. In Pôle universitaire de santé numérique de l’Estrie.

• H. Laribi , N. Raymond , M. Vallières (2024), Improving Mortality Prediction with Longitudinal Data. In Pôle universitaire de santé numérique de l’Estrie.

• H. Laribi , N. Raymond , M. Vallières (2023), Graph neural networks for predicting patient mortality within one year of hospital admission. In T-CAIREM.

• O. Lefebvre , M. Vallières (2023), Investigating model failures by patient (profiles) for safer clinical deployment . In T-CAIREM.

• M. Ait Lhaj Loutfi (2022), A compliant package with international standards for radiomics analysis of medical images. In CIRIUS.

• H. Laribi (2022), Graph neural networks for predicting patient mortality within one year of hospital admission. In CIRIUS.

• O. Lefebvre (2022), Precision analysis of predictive uncertainty of machine learning models in medical context. In CIRIUS.

• M. Ait Lhaj Loutfi (2022), An international standard compliant tool for radiomic feature extraction from medical images. In Medical Imaging with Deep Learning 2022.

• A. Ayotte (2021), 3D convolutional neural network as a decision support system for multiple renal tumor classification tasks. In ACFAS 2021.

• N. Raymond (2021), Integration of genomic data in the design of learning models in precision oncology. In ACFAS 2021.