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Ntorina Thanou

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Related units (4)

Signal Processing Laboratory 4

EPFL
STI
IEM
LTS4

Laboratory of Mathematical Data Science (IC/SB)

EPFL
IC
IINFCOM
MDS2

Chair of Mathematical Data Science (SB/IC)

EPFL
SB
MATH
MDS1

AI Center - Administration

EPFL
EPFL
VPA-AVP-CP
VPA
AICENTER
AICENTER
AICENTER-GE
AICENTER-GE

Courses taught by this person (1)

EE-740: ELLIS Summer School on AI for Health

The EPFL AI Center and the ELLIS EPFL unit are organizing the AI for Health Summer School, taking place on the EPFL campus from 7th to 11th July, 2025. This intensive week will delve into how AI is transforming biomedicine, with a focus on the intersection ...

Related publications (39)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

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

Emmanuel Abbé, Ortal Yona Senouf, Thabodhan Mahendiran, Ntorina Thanou, Edward Carlo Giorgio Andò

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 ...

Elsevier BV2024

Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion

Pascal Frossard, Ntorina Thanou

Graph-based representation approaches have been proven to be successful in the analysis of biomedical data due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in ...

Springer International Publishing Ag2024

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Pascal Frossard, Emmanuel Abbé, Ortal Yona Senouf, Ntorina Thanou, Ivan-Daniel Sievering

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 ...

2024