Nathan Painchaud

Nathan Painchaud

Postdoctoral researcher

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification

Published in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (IEEE TUFFC), 2025

Drawing on novel Transformer models applied to tabular data, we propose a method that considers all descriptors extracted from medical records and echocardiograms to learn the representation of a cardiovascular pathology with a difficult-to-characterize continuum.

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," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 72, no. 10, pp. 1388-1400, Oct. 2025.
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Neural Teleportation

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.
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Echocardiography Segmentation With Enforced Temporal Consistency

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.
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Cardiac Segmentation with Strong Anatomical Guarantees

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.
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Conference Papers

Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification

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.
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Extraction of Volumetric Indices from Echocardiography: Which Deep Learning Solution for Clinical Use?

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.
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On the effectiveness of GAN generated cardiac MRIs for segmentation

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.
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Cardiac MRI Segmentation with Strong Anatomical Guarantees

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.
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