Abstract
Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds further difficulty to accurate diagnosis. To address these, we propose a personalized diagnostic tool that can customize its decision-making process for different patients. It consists of a multi-view classification module for feature extraction and a personalized weighting allocation network that generates optimal weighting for different views. It is also equipped with a self-supervised view-aware contrastive loss to further improve the model robustness towards different patient groups. Experimental results show that the proposed framework can better utilize multi-view information and outperform the competing methods.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 62101342, 62171290, and 61901275); Shenzhen-Hong Kong Joint Research Program (No. SGDX20201103095613036), Shenzhen Science and technology research and Development Fund for Sustainable development project (No. KCXFZ20201221173613036); National Natural Science Foundation of China (No. 82071928); Shenzhen University Startup Fund (2019131).
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Huang, H. et al. (2022). Personalized Diagnostic Tool for Thyroid Cancer Classification Using Multi-view Ultrasound. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_64
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DOI: https://doi.org/10.1007/978-3-031-16437-8_64
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