ArticleJournal of Cancer2025
Evaluation of Band Selection for Spectrum-Aided Visual Enhancer (SAVE) for Esophageal Cancer Detection.
Article in Journal of Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Multimodal-multiscale hybrid fusion network for automated esophageal cancer diagnosis.iScience · 2026Article
- Comparison of the diagnostic value of white light endoscopic, narrow band imaging, and iodine staining individually and in combination for early esophageal cancer and precancerous lesions.Frontiers in oncology · 2026Article
- Deep learning-based automatic segmentation and CT elastography radiomics for preoperative prediction of right recurrent laryngeal nerve lymph node metastasis in esophageal cancer.Frontiers in oncology · 2026Article
- Transforming Endoscopic Image Classification with Spectrum-Aided Vision for Early and Accurate Cancer Identification.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning-based single-shot computational spectrometer using multilayer thin films.Scientific reports · 2025Article
- Unique Function in Cancer Stemness and Prognostic Significance ofBiomedicines · 2025Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Optimization strategies for ultrasound zoning of cervical lymph nodes in esophageal cancer: a precision medicine approach.Frontiers in oncology · 2025Review
- Feasibility study for proton dose calculation of esophageal squamous cell carcinoma based on stopping power ratio directly derived from dual energy CT.Frontiers in oncology · 2025Article
- Advancing artificial intelligence applicability in endoscopy through source-agnostic camera signal extraction from endoscopic images.PloS one · 2025Article
- Self-supervised learning framework for efficient classification of endoscopic images using pretext tasks.PloS one · 2025Article
- Development and validation of esophageal cancer prediction model based on DeepSurv model: A cohort study.Science progressArticle
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6 authors.
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Abstract
Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. In medical imaging, narrow-band imaging (NBI) is an imaging technique for endoscopic diagnostic medical tests, where light of specific blue and green wavelengths is used to enhance the detail of certain aspects of the surface of the mucosa. A special filter is electronically activated by a switch in the endoscope leading to the use of ambient light of wavelengths of 415 nm (blue) and 540 nm (green). Because the peak light absorption of hemoglobin occurs at these wavelengths, blood vessels will appear very dark, allowing for their improved visibility and in the improved identification of other surface structures. NBI when compared with the white-light imaging (WLI) have proven to have better precision when combined with computer-aided diagnosis (CAD, Intespec C, Hitspectra Intelligent Technology Co., Kaohsiung, Taiwan) in detecting cancerous images. NBI endoscopes are specialized equipment that may not be widely available in all healthcare settings. By leveraging existing WLI endoscopic systems and developing algorithms to simulate NBI imaging, healthcare facilities can achieve similar di-agnostic capabilities without the need for additional costly equipment. Therefore, in this study, algorithm known as the SAVE (spectrum-aided visual enhancer) has been developed which can simulate NBI from the WLI images through an intelligent band-selective hyperspectral imaging for Olympus endoscope. The results suggested that the SAVE-NBI images had a better precision and F1-score than the WLI images.
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