@inproceedings{c620aca6068d4db482571f4801bfcac0,
title = "A Video Transformer Network for Thyroid Cancer Detection on Hyperspectral Histologic Images",
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.",
keywords = "Hyperspectral imaging, deep learning, image classification, thyroid cancer, transformer attention-based neural networks, whole-slide imaging",
author = "Tran, {Minh Ha} and Ofelia Gomez and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Medical Imaging 2023: Digital and Computational Pathology ; Conference date: 19-02-2023 Through 23-02-2023",
year = "2023",
doi = "10.1117/12.2654851",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Tomaszewski, {John E.} and Ward, {Aaron D.}",
booktitle = "Medical Imaging 2023",
}