Deep learning-based diagnosis of thyroid tumors using histopathology images from thyroid nodule capsule

Nitya A. Shah; Jinal Suthar; Tejaswee A.; Adrian Enache; Lucian G. Eftimie; Radu Hristu; Angshuman Paul

doi:10.1117/12.3006242

3 April 2024 Deep learning-based diagnosis of thyroid tumors using histopathology images from thyroid nodule capsule

Nitya A. Shah, Jinal Suthar, Tejaswee A., Adrian Enache, Lucian G. Eftimie, Radu Hristu, Angshuman Paul

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129273D (2024) https://doi.org/10.1117/12.3006242
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Histopathology analysis of thyroid nodule is the current gold standard for the differential diagnosis of thyroid tumors. Deep learning methods have been extensively used for the diagnosis of histopathology images. We look into the possibility of the differential diagnosis of thyroid tumors by analysing histopathology images of thyroid nodule capsules using different deep learning methods. Residual Network (ResNet), Densely Connected Network (DenseNet) and Vision Transformer (ViT). Our study shows the superiority of the histopathology images of thyroid nodule capsules for the differential diagnosis of thyroid tumors compared to histopathology images of thyroid nodules.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nitya A. Shah, Jinal Suthar, Tejaswee A., Adrian Enache, Lucian G. Eftimie, Radu Hristu, and Angshuman Paul "Deep learning-based diagnosis of thyroid tumors using histopathology images from thyroid nodule capsule", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129273D (3 April 2024); https://doi.org/10.1117/12.3006242

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