ArticleProceedings of SPIE--the International Society for Optical Engineering2017
Deep Learning based Classification for Head and Neck Cancer Detection with Hyperspectral Imaging in an Animal Model.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04589884 (Intraoperative EXamination Using MAChine-learning-based HYperspectral for diagNosis & Autonomous Anatomy Assessment), which is not on this map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Intraoperative EXamination Using MAChine-learning-based HYperspectral for diagNosis & Autonomous Anatomy Assessment
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Hyperspectral imaging for tumor resection guidance in surgery: a systematic review of preclinical and clinical studies.Journal of biomedical optics · 2025Pooled it
- Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.Scientific reports · 2025Article
- Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissuesJournal of biomedical optics · 2023Article
- Deep Learning in Medical Hyperspectral Images: A Review.Sensors (Basel, Switzerland) · 2022Review
- Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model.Multimedia tools and applications · 2022Article
- Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection.Diagnostics (Basel, Switzerland) · 2021Article
- Comprehensive review of surgical microscopes: technology development and medical applications.Journal of biomedical optics · 2021Review
- Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery.Clinical and experimental otorhinolaryngology · 2020Review
- Article
- Different Machine Learning and Deep Learning Methods for the Classification of Colorectal Cancer Lymph Node Metastasis Images.Frontiers in bioengineering and biotechnology · 2020Article
- Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method.Biomedical optics express · 2019Article
- Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.Journal of imaging · 2019Review
- Adaptive deep learning for head and neck cancer detection using hyperspectral imaging.Visual computing for industry, biomedicine, and art · 2019Article
- Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging.Journal of biomedical optics · 2017Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.
Indexed as
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.