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Thyroid Carcinoma Detection on Whole Histologic Slides Using Hyperspectral Imaging and Deep Learning.
- Source :
-
Proceedings of SPIE--the International Society for Optical Engineering [Proc SPIE Int Soc Opt Eng] 2022 Feb-Mar; Vol. 12039. Date of Electronic Publication: 2022 Apr 04. - Publication Year :
- 2022
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Abstract
- Hyperspectral imaging (HSI), a non-invasive imaging modality, has been successfully used in many different biological and medical applications. One such application is in the field of oncology, where hyperspectral imaging is being used on histologic samples. This study compares the performances of different image classifiers using different imaging modalities as training data. From a database of 33 fixed tissues from head and neck patients with follicular thyroid carcinoma, we produced three different datasets: an RGB image dataset that was acquired from a whole slide image scanner, a hyperspectral (HS) dataset that was acquired with a compact hyperspectral camera, and an HS-synthesized RGB image dataset. Three separate deep learning classifiers were trained using the three datasets. We show that the deep learning classifier trained on HSI data has an area under the receiver operator characteristic curve (AUC-ROC) of 0.966, higher than that of the classifiers trained on RGB and HSI-synthesized RGB data. This study demonstrates that hyperspectral images improve the performance of cancer classification on whole histologic slides. Hyperspectral imaging and deep learning provide an automatic tool for thyroid cancer detection on whole histologic slides.<br />Competing Interests: DISCLOSURES This work has not been submitted for presentation or publication elsewhere. The authors have no potential conflicts of interest to disclose. Informed consent was obtained from all patients in accordance with Emory Institutional Review Board policies under the Head and Neck Satellite Tissue Bank (HNSB, IRB00003208) protocol.
Details
- Language :
- English
- ISSN :
- 0277-786X
- Volume :
- 12039
- Database :
- MEDLINE
- Journal :
- Proceedings of SPIE--the International Society for Optical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 36798939
- Full Text :
- https://doi.org/10.1117/12.2612963