ArticleJournal of biomedical optics2024
Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks.
Article in Journal of biomedical optics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 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
- Portable Multispectral Optoelectronic System for Thyroid Cancer Detection.Sensors (Basel, Switzerland) · 2026Article
- Polarized hyperspectral and polarized light microscopic imaging for enhanced visualization of white blood cells.Journal of biomedical optics · 2026Article
- Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning.Parasites & vectors · 2026Article
- Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.Journal of cancer research and clinical oncology · 2025Article
- A comparative analysis of deep learning architectures for thyroid tissue classification with hyperspectral imaging.Scientific reports · 2025Article
- Special Section Guest Editorial: JBO Special Section on Hyperspectral Imaging.Journal of biomedical optics · 2025Article
- Research on Blood Cell Image Detection Method Based on Fourier Ptychographic Microscopy.Sensors (Basel, Switzerland) · 2025Article
- Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Significance: Hyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology. Aim: The study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods. Approach: Using an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension Results: In the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted Conclusions: The TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the
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Registered trials
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