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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.
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Minh Ha Tran, Ofelia Gomez, 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