Noise-induced self-supervised hybrid UNet transformer for ischemic stroke segmentation with limited data annotations

Noise-induced self-supervised hybrid UNet transformer for ischemic stroke segmentation with limited data annotations

Abstract We extend the Hybrid Unet Transformer (HUT) foundation model, which combines the advantages of the CNN and Transformer architectures with a noisy self-supervised approach, and demonstrate it in an ischemic stroke lesion segmentation task. We introduce a self-supervised approach using a nois...

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Bibliographic Details
Main Authors: Wei Kwek Soh, Jagath C. Rajapakse
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Reports
Subjects:
Self-supervised
Limited annotation
Ischemic stroke segmentation
Noise regularisation
Small dataset
Online Access:https://doi.org/10.1038/s41598-025-04819-2
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https://doi.org/10.1038/s41598-025-04819-2

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