Enhancing head and neck cancer detection accuracy in digitized whole-slide histology with the HNSC-classifier: a deep learning approach

Enhancing head and neck cancer detection accuracy in digitized whole-slide histology with the HNSC-classifier: a deep learning approach

Head and neck squamous cell carcinoma (HNSCC) represents the sixth most common cancer worldwide, with pathologists routinely analyzing histological slides to diagnose cancer by evaluating cellular heterogeneity, a process that remains time-consuming and labor-intensive. Although no previous studies...

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Bibliographic Details
Main Authors: Haiyang Yu, Wang Yu, Yuan Enwu, Jun Ma, Xin Zhao, Linlin Zhang, Fang Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Molecular Biosciences
Subjects:
HNSCC
histopathology image
deep learning
cancer diagnosis
WSI
Online Access:https://www.frontiersin.org/articles/10.3389/fmolb.2025.1652144/full
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