Publications by Ortal Yona Senouf

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Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI ...

In patients with stable Coronary Artery Disease (CAD), the identification of lesions which will be responsible of a myocardial infarction (MI) during follow-up remains a daily challenge. In this work, we propose to predict culprit stenosis by applying a de ...

The goal of this work is to investigate the ability of transfer learning (TL) and multitask learning (MTL) algorithms to predict tasks related to myocardial infarction (MI) in a small-data regime, leveraging a larger dataset of haemodynamic targets. The da ...

BackgroundAngiographic parameters can facilitate the risk stratification of coronary lesions but remain insufficient in the prediction of future myocardial infarction (MI).AIMSWe compared the ability of humans, angiographic parameters and deep learning (DL ...

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional c ...

Ortal Yona Senouf

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Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Predicting future myocardial infarction from angiographies with deep learning

Can Knowledge Transfer Techniques Compensate for the Limited Myocardial Infarction Data by Leveraging Haemodynamics? An in silico Study

Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study

AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation