ArticleProceedings of SPIE--the International Society for Optical Engineering2025
An automatic processing framework for hyperspectral histologic images and benchmark dataset.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Optimization of Transfer Learning of Foundation Models for Hyperspectral Histologic Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- A spatial-spectral vision transformer model for head and neck cancer detection with hyperspectral, RGB, and synthesized RGB histologic images.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Hyperspectral imaging (HSI) is an emerging imaging modality for histopathological applications. However, annotations on RGB histological images are usually used as the reference standard. To use and validate hyperspectral data, it is critical to correlate each hyperspectral image with the corresponding region in a whole-slide image and retrieve accurate tissue label. In this work, we developed a fully automated processing pipeline for hyperspectral histological images. Given a high-resolution digitized whole-slide histological image, the annotation, and a hyperspectral image tile of any region in the slide, the proposed method can locate the HSI tile region in the whole-slide image, crop the RGB image tile and tissue label, and align the RGB tile and tissue label to the HSI tile. With our proposed processing pipeline, we collected and formed a dataset with over 350 whole-slide hyperspectral histological images of human head and neck cancers. The proposed processing pipeline can serve as a general tool for fast and automated hyperspectral histological images, thus facilitating the adaptation of hyperspectral imaging in digital pathology to assist automatic histology diagnosis.
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