Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer

Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer

Abstract One drawback of existing artificial intelligence (AI)-based histopathological prediction models is the lack of interpretability. The objective of this study is to extract p16-positive oropharyngeal squamous cell carcinoma (OPSCC) features in a form that can be interpreted by pathologists us...

Full description

Bibliographic Details
Main Authors: Masahiro Adachi, Tetsuro Taki, Naoya Sakamoto, Motohiro Kojima, Akihiko Hirao, Kazuto Matsuura, Ryuichi Hayashi, Keiji Tabuchi, Shumpei Ishikawa, Genichiro Ishii, Shingo Sakashita
Format: Article
Language:English
Published: Nature Portfolio 2024-02-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-55288-y

Internet

https://doi.org/10.1038/s41598-024-55288-y

Similar Items