Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology

Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology

Abstract Deep learning models require large amounts of annotated data, which are hard to obtain in the medical field, as the annotation process is laborious and depends on expert knowledge. This data scarcity hinders a model’s ability to generalise effectively on unseen data, and recently, foundatio...

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Bibliographic Details
Main Authors: Mateja Napravnik, Franko Hržić, Martin Urschler, Damir Miletić, Ivan Štajduhar
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Transfer learning
Foundation models
RadiologyNET
Model pretraining
Segmentation
Regression
Online Access:https://doi.org/10.1038/s41598-025-05009-w
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https://doi.org/10.1038/s41598-025-05009-w

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