126 citations · 209 across the 9 of their papers we have counts for
10 papers
Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need
Nathan Painchaud, Tristan Habémont, Morgane des Ligneris +6
Risk stratification for pulmonary embolism (PE) is critical for clinical decision-making. Stratification guidelines are based on patient medical records, parameters measured from c…
A Patient-Specific Pulmonary Arterial Tree Digital Twin to Extract Pulmonary Embolism Biomarkers
Morgane des Ligneris, Nathan Painchaud, Allan Serva +4
Pulmonary embolism, the obstruction of a pulmonary artery by a blood clot, is one of the leading causes of acute cardiovascular syndrome. In clinical practice, therapeutic decision…
DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
Jérémie Stym-Popper, Nathan Painchaud, Clément Rambour +3
Multimodal data fusion is a key approach for enhancing diagnosis in medical applications. We propose an asymmetric fusion strategy starting from a primary modality and integrating…
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Nathan Painchaud, Jérémie Stym-Popper, Pierre-Yves Courand +4
Deep learning enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain. These descriptors provi…
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?
Hang Jung Ling, Nathan Painchaud, Pierre-Yves Courand +3
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by e…
Echocardiography Segmentation with Enforced Temporal Consistency
Nathan Painchaud, Nicolas Duchateau, Olivier Bernard +1
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer…
Neural Teleportation
Marco Armenta, Thierry Judge, Nathan Painchaud +5
In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks. Neural teleportation "telep…
Cardiac Segmentation with Strong Anatomical Guarantees
Nathan Painchaud, Youssef Skandarani, Thierry Judge +3
Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation…
On the effectiveness of GAN generated cardiac MRIs for segmentation
Youssef Skandarani, Nathan Painchaud, Pierre-Marc Jodoin +1
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 ground…
Cardiac MRI Segmentation with Strong Anatomical Guarantees
Nathan Painchaud, Youssef Skandarani, Thierry Judge +3
Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a gre…