Nathan Painchaud

Nathan Painchaud

(he/him)

Postdoctoral researcher

CREATIS (INSA Lyon)

Professional Summary

I am currently a postdoctoral researcher at the CREATIS laboratory (INSA Lyon, France), where I work on multimodal learning on graphs and clinical data for pulmonary embolism risk stratification models.

Prior to my post-doc, I obtained my joint Ph.D. between the Université de Sherbrooke (Canada) and Institut National des Sciences Appliquées de Lyon (France), working on deep representation learning for cardiac medical image analysis.

Education

Assistant Professor Qualification

Conseil National des Universités (CNU)

Ph.D. Computer Science (Joint Supervision)

Université de Sherbrooke (Canada) / INSA Lyon (France)

M.Sc. Computer Science

Université de Sherbrooke (Canada)

B.Sc. Computer Science

Université de Sherbrooke (Canada)

Interests

Representation Learning Graph Learning Population Representation Quantitative Medical Image Analysis Multimodal Medical Data Fusion
📚 My Research

I’m a junior researcher working on applications of AI to healthcare data. I am mostly interested in how to combine the complex multimodal data, especially the rich imaging data, to improve disease characterization.

From my background in computer science, I’ve also kept a strong interest in the technical side of things. I try my best to be active on GitHub and to contribute to the open-source projects that I use, either professionally or personally.

Don’t hesitate to reach out to collaborate 😃

Featured Publications
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification featured image

Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification

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 …

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Echocardiography Segmentation With Enforced Temporal Consistency featured image

Echocardiography Segmentation With Enforced Temporal Consistency

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 …

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Nathan Painchaud
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Cardiac Segmentation With Strong Anatomical Guarantees featured image

Cardiac Segmentation With Strong Anatomical Guarantees

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 …

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Featured Talks
Attention and Transformers featured image

Attention and Transformers

Lecture on attention and Transformers given at the Deep Learning for Medical Imaging (DLMI) 2025 spring school.

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Deep manifold learning for improved high blood pressure characterization using echocardiography featured image

Deep manifold learning for improved high blood pressure characterization using echocardiography

PhD Thesis Defense of Joint Thesis between Université de Sherbrooke and INSA Lyon.

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Nathan Painchaud
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