Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions

Abstract Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets have demonstrated impressive capabilities of...

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Bibliographic Details
Main Authors: Emese Sükei, Elisabeth Rumetshofer, Niklas Schmidinger, Andreas Mayr, Ursula Schmidt-Erfurth, Günter Klambauer, Hrvoje Bogunović
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Multi-modal imaging
Contrastive pre-training
Representation learning
Predictive modeling
Retinal imaging
Online Access:https://doi.org/10.1038/s41598-024-78515-y
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https://doi.org/10.1038/s41598-024-78515-y

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