Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology
Mateja Napravnik; Franko Hržić; Martin Urschler; Damir Miletić; Ivan Štajduhar · 2025 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
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…
Excerpt shown for reference under fair use — read the full paper at the publisher.
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WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1038/s41598-025-05009-w