ArticleScientific reports2019
Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 115 citations in OpenAlex.
- Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer.Head and neck pathology · 2026Review
- Classification of esophageal cancer by using hyperspectral data.International journal of computer assisted radiology and surgery · 2026Article
- Artificial Intelligence Basics for Head and Neck Pathologists: A Simple Guide.Head and neck pathology · 2026Review
- Weakly supervised regression enables interpretable tumor detection in whole-slide histopathology without negative cases.Scientific reports · 2025Article
- AI-driven prediction of progression to oral squamous cell carcinoma using a multiresolution pathology model.NPJ digital medicine · 2025Article
- Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma.NPJ digital medicine · 2025Article
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Improving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement.Scientific reports · 2025Article
- An automatic processing framework for hyperspectral histologic images and benchmark dataset.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- A spatial-spectral vision transformer model for head and neck cancer detection with hyperspectral, RGB, and synthesized RGB histologic images.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Enhancing head and neck cancer detection accuracy in digitized whole-slide histology with the HNSC-classifier: a deep learning approach.Frontiers in molecular biosciences · 2025Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks.Journal of biomedical optics · 2024Article
- Article
- Oral epithelial dysplasia detection and grading in oral leukoplakia using deep learning.BMC oral health · 2024Article
- Handheld hyperspectral imaging as a tool for the post-mortem interval estimation of human skeletal remains.Heliyon · 2024Article
- An Overview of Advances in Rare Cancer Diagnosis and Treatment.International journal of molecular sciences · 2024Review
- Polarized hyperspectral microscopic imaging system for enhancing the visualization of collagen fibers and head and neck squamous cell carcinoma.Journal of biomedical optics · 2024Article
- Explainable convolutional neural networks for assessing head and neck cancer histopathology.Diagnostic pathology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Primary management for head and neck cancers, including squamous cell carcinoma (SCC), involves surgical resection with negative cancer margins. Pathologists guide surgeons during these operations by detecting cancer in histology slides made from the excised tissue. In this study, 381 digitized, histological whole-slide images (WSI) from 156 patients with head and neck cancer were used to train, validate, and test an inception-v4 convolutional neural network. The proposed method is able to detect and localize primary head and neck SCC on WSI with an AUC of 0.916 for patients in the SCC testing group and 0.954 for patients in the thyroid carcinoma testing group. Moreover, the proposed method is able to diagnose WSI with cancer versus normal slides with an AUC of 0.944 and 0.995 for the SCC and thyroid carcinoma testing groups, respectively. For comparison, we tested the proposed, diagnostic method on an open-source dataset of WSI from sentinel lymph nodes with breast cancer metastases, CAMELYON 2016, to obtain patch-based cancer localization and slide-level cancer diagnoses. The experimental design yields a robust method with potential to help create a tool to increase efficiency and accuracy of pathologists detecting head and neck cancers in histological images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.