Work

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

Year: 2023

Type: conference-paper

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.

Source: SPIE Medical Imaging: Digital and Computational Pathology

DOI: 10.1117/12.2654851

Authors: Minh H. Tran, Ofelia Gomez, Baowei Fei

Institutions: The University of Texas at Dallas, The University of Texas Southwestern Medical Center

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Cancer Prevention and Research Institute of Texas, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute