A method for spatial interpretation of weakly supervised deep learning models in computational pathology

A method for spatial interpretation of weakly supervised deep learning models in computational pathology

Abstract Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or histological grading). In the weakly sup...

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Bibliographic Details
Main Authors: Abhinav Sharma, Bojing Liu, Mattias Rantalainen
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Reports
Subjects:
Deep learning
Image analysis
Computation pathology
Weakly supervised learning
Spatial interpretability
Online Access:https://doi.org/10.1038/s41598-025-04043-y
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https://doi.org/10.1038/s41598-025-04043-y

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