Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning

Minh Ha Tran; Ling Ma; James V. Litter; Amy Y. Chen; Baowei Fei

doi:10.1117/12.2612963

4 April 2022 Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning

Minh Ha Tran, Ling Ma, James V. Litter, Amy Y. Chen, Baowei Fei

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120390H (2022) https://doi.org/10.1117/12.2612963
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Minh Ha Tran, Ling Ma, James V. Litter, Amy Y. Chen, and Baowei Fei "Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120390H (4 April 2022); https://doi.org/10.1117/12.2612963

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