Published in Mathematics, 2023
We explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks.
Recommended citation: M. Armenta, T. Judge, N. Painchaud, Y. Skandarani, C. Lemaire, G. G. Sanchez, P. Spino, and P.-M. Jodoin, "Neural Teleportation," Mathematics, vol. 11, no. 2, pp. 480, Jan. 2023.
Download Paper | Download Bibtex
Published in IEEE Transactions on Medical Imaging (IEEE TMI), 2022
In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints.
Recommended citation: N. Painchaud, N. Duchateau, O. Bernard, and P.-M. Jodoin, "Echocardiography Segmentation With Enforced Temporal Consistency," IEEE Transactions on Medical Imaging, vol. 41, no. 10, pp. 2867–2878, Oct. 2022.
Download Paper | Download Slides | Download Bibtex
Published in IEEE Transactions on Medical Imaging (IEEE TMI), 2020
In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability.
Recommended citation: N. Painchaud, Y. Skandarani, T. Judge, O. Bernard, A. Lalande, and P.-M. Jodoin, "Cardiac Segmentation with Strong Anatomical Guarantees," IEEE Transactions on Medical Imaging, vol. 39, no. 11, pp. 3703–3713, Nov. 2020.
Download Paper | Download Slides | Download Bibtex
Published in Medical Imaging with Deep Learning (MIDL), 2024
Drawing on novel transformer models applied to tabular data (e.g. variables from electronic health records), we propose a method that considers descriptors extracted from medical records and echocardiograms to learn a representation of hypertension.
Recommended citation: N. Painchaud, P.-Y. Courand, P.-M. Jodoin, N. Duchateau, and O. Bernard, "Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification," presented at Medical Imaging with Deep Learning (MIDL), 2024.
Download Paper | Download Bibtex
Published in Functional Imaging and Modeling of the Heart (FIMH), 2023
We propose a comprehensive comparison between the current best performing methods in medical/echocardiographic image segmentation, with a particular focus on temporal consistency and cross-dataset aspects.
Recommended citation: H. J. Ling, N. Painchaud, P.-Y. Courand, P.-M. Jodoin, D. Garcia, and O. Bernard, "Extraction of Volumetric Indices from Echocardiography: Which Deep Learning Solution for Clinical Use?," in proc. Functional Imaging and Modeling of the Heart (FIMH), 2023, pp. 245-254.
Download Paper | Download Bibtex
Published in Medical Imaging with Deep Learning (MIDL), 2020
In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation.
Recommended citation: Y. Skandarani, N. Painchaud, P.-M. Jodoin, and A. Lalande, "On the effectiveness of GAN generated cardiac MRIs for segmentation," presented at Medical Imaging with Deep Learning (MIDL), 2020.
Download Paper | Download Bibtex
Published in Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results.
Recommended citation: N. Painchaud, Y. Skandarani, T. Judge, O. Bernard, A. Lalande, and P.-M. Jodoin, "Cardiac MRI Segmentation with Strong Anatomical Guarantees," in proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019, pp. 632–640.
Download Paper | Download Slides | Download Bibtex