A video transformer network for thyroid cancer detection on hyperspectral histologic images

Minh Ha Tran; Ofelia Gomez; Baowei Fei

doi:10.1117/12.2654851

6 April 2023 A video transformer network for thyroid cancer detection on hyperspectral histologic images

Minh Ha Tran, Ofelia Gomez, Baowei Fei

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 1247107 (2023) https://doi.org/10.1117/12.2654851
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Hyperspectral imaging is a label-free and non-invasive imaging modality that seeks to capture images in different wavelengths. In this study, we used a vision transformer that was pre-trained from video data to detect thyroid cancer on hyperspectral images. We built a dataset of 49 whole slide hyperspectral images (WS-HSI) of thyroid cancer. To improve training, we introduced 5 new data augmentation methods that transform spectra. We achieved an F-1 score of 88.1% and an accuracy of 89.64% on our test dataset. The transformer network and the whole slide hyperspectral imaging technique can have many applications in digital pathology.

Conference Presentation

Citation Download Citation

Minh Ha Tran, Ofelia Gomez, and Baowei Fei "A video transformer network for thyroid cancer detection on hyperspectral histologic images", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 1247107 (6 April 2023); https://doi.org/10.1117/12.2654851

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