ArticleBiomedical optics express2019
Tumor tissue classification based on micro-hyperspectral technology and deep learning.
Article in Biomedical optics express, 2019. 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 23 papers, 1 of them a synthesis 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- AI-Powered TAT-Net Hyperspectral Imaging for Rapid and Accurate Differentiation of Sinonasal Inverted Papilloma and Malignancy.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Application of Laser-Induced Breakdown Spectroscopy (LIBS) technology in malignant tumor diagnosis.Discover oncology · 2026Review
- Visible-Light Hyperspectral Reconstruction and PCA-Based Feature Extraction for Malignant Pleural Effusion Cytology.Biosensors · 2025Article
- Enhancing Oral Health Diagnostics With Hyperspectral Imaging and Computer Vision: Clinical Dataset Study.JMIR medical informatics · 2025Article
- Improving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement.Scientific reports · 2025Article
- Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.Sensors (Basel, Switzerland) · 2024Article
- Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features.Diagnostics (Basel, Switzerland) · 2023Article
- Nondestructive detection ofFrontiers in plant science · 2023Article
- Classification of neck tissues in OCT images by using convolutional neural network.Lasers in medical science · 2022Article
- Deep Learning in Medical Hyperspectral Images: A Review.Sensors (Basel, Switzerland) · 2022Review
- MC-GAT: multi-layer collaborative generative adversarial transformer for cholangiocarcinoma classification from hyperspectral pathological images.Biomedical optics express · 2022Article
- Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging.Journal of biomedical optics · 2022Article
- A Multi-Task Convolutional Neural Network for Lesion Region Segmentation and Classification of Non-Small Cell Lung Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model.Scientific reports · 2022Article
- Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging.Scientific reports · 2022Article
- New Intraoperative Imaging Tools and Image-Guided Surgery in Gastric Cancer Surgery.Diagnostics (Basel, Switzerland) · 2022Review
- Hyperspectral Imaging during Normothermic Machine Perfusion-A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks.Biomedicines · 2022Article
- Identification of gastric cancer with convolutional neural networks: a systematic review.Multimedia tools and applications · 2022Article
- Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to explore the application of hyperspectral technology in the pathological diagnosis of tumor tissue, we used microscopic hyperspectral imaging technology to establish a hyperspectral database of 30 patients with gastric cancer. Based on the difference in spectral-spatial features between gastric cancer tissue and normal tissue in the wavelength of 410-910 nm, we propose a deep-learning model-based analysis method for gastric cancer tissue. The microscopic hyperspectral feature and individual difference of gastric tissue, spatial-spectral joint feature and medical contact are studied. The experimental results show that the classification accuracy of proposed model for cancerous and normal gastric tissue is 97.57%, the sensitivity and specificity of gastric cancer tissue are 97.19% and 97.96% respectively. Compared with the shallow learning method, CNN can fully extract the deep spectral-spatial features of tumor tissue. The combination of deep learning model and micro-spectral analysis provides new ideas for the research of medical pathology.
Identifiers
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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.