ArticleProceedings of SPIE--the International Society for Optical Engineering2020
Using a 22-Layer U-Net to Perform Segmentation of Squamous Cell Carcinoma on Digitized Head and Neck Histological Images.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- AI-driven prediction of progression to oral squamous cell carcinoma using a multiresolution pathology model.NPJ digital medicine · 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
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Artificial intelligence in head and neck cancer diagnosis.Journal of pathology informatics · 2022Review
- Article
- Comparison of texture-based classification and deep learning for plantar soft tissue histology segmentation.Computers in biology and medicine · 2021Article
- Hyperspectral Microscopic Imaging for the Detection of Head and Neck Squamous Cell Carcinoma on Histologic Slides.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Squamous cell carcinoma (SCC) comprises over 90 percent of tumors in the head and neck. The diagnosis process involves performing surgical resection of tissue and creating histological slides from the removed tissue. Pathologists detect SCC in histology slides, and may fail to correctly identify tumor regions within the slides. In this study, a dataset of patches extracted from 200 digitized histological images from 84 head and neck SCC patients was used to train, validate and test the segmentation performance of a fully-convolutional U-Net architecture. The neural network achieved a pixel-level segmentation AUC of 0.89 on the testing group. The average segmentation time for whole slide images was 72 seconds. The training, validation, and testing process in this experiment produces a model that has the potential to help segment SCC images in histological images with improved speed and accuracy compared to the manual segmentation process performed by pathologists.
Identifiers
What OpenQuestion holds
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.