ArticleSensors (Basel, Switzerland)2020
Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 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
- Hyperspectral Imaging of Whole-Cell Region for Differentiating Cervical Squamous Intraepithelial Lesion Cytology.Cancer medicine · 2026Article
- HMI-LUSC: A Histological Hyperspectral Imaging Dataset for Lung Squamous Cell Carcinoma.Scientific data · 2026Article
- Image Feature Fusion of Hyperspectral Imaging and MRI for Automated Subtype Classification and Grading of Adult Diffuse Gliomas According to the 2021 WHO Criteria.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal Integration Enhances Tissue Image Information Content: A Deep Feature Perspective.Bioengineering (Basel, Switzerland) · 2025Article
- AI-Powered Spectral Imaging for Virtual Pathology Staining.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- A high-speed hyperspectral imaging system and large-scale hyperspectral dataset for abdominal surgical applications.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Masked image modeling in medical hyperspectral imaging: reconstruction evaluation and downstream tasks.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review.APL bioengineering · 2024Review
- Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma.Cancers · 2024Review
- Technological Frontiers in Brain Cancer: A Systematic Review and Meta-Analysis of Hyperspectral Imaging in Computer-Aided Diagnosis Systems.Diagnostics (Basel, Switzerland) · 2024Review
- Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning.Science advances · 2024Article
- Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks.Healthcare technology letters · 2024Article
- Histological Hyperspectral Glioblastoma Dataset (HistologyHSI-GB).Scientific data · 2024Article
- Preclinical Application of CEST MRI to Detect Early and Regional Tumor Response to Local Brain Tumor Treatment.Pharmaceutics · 2024Article
- Intelligent tumor tissue classification for Hybrid Health Care Units.Frontiers in medicine · 2024Article
- Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection.NPJ precision oncology · 2023Article
- Multispectral Imaging Method for Rapid Identification and Analysis of Paraffin-Embedded Pathological Tissues.Journal of digital imaging · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.
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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.