Reproducibility of digital pathology features extracted from deep learning and foundational AI models on sequential tissue slides

Abstract Numerous deep learning (DL) and foundational models (FMs) designed for digital pathology analysis employ advanced feature extraction techniques from digitized hematoxylin and eosin (H&E) slides obtained from tissue sections. These extracted features may serve as the input for complex models...

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Opis bibliograficzny

Główni autorzy:	Jagadheshwar Balan, Nicholas B. Larson
Format:	Artigo
Język:	Inglês
Wydane:	Nature Portfolio 2025-12-01
Seria:	Scientific Reports
Hasła przedmiotowe:	Digital pathology Nuclei segmentation Cell type features Spatial features Foundation model embeddings
Dostęp online:	https://doi.org/10.1038/s41598-025-30947-w
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