Abstract
Precise thyroid nodule classification is a key issue in endocrine clinic domain, which can enhance a patient’s chance for survival. The reports of type-B ultrasound examination are important data source for thyroid nodule classification, and patients with thyroid nodules normally undergo several periodic ultrasound examinations during the process of diagnosis and treatment. However, most of the existing methods rely on feature engineering of single ultrasound reports and they did not take into consideration the historical records of the patients. In this paper, we propose a Hierarchical Recurrent Neural Network (HRNN) for thyroid nodule classification using historical ultrasound reports. HRNN consists of three layers of Long Short-Term Memory (LSTM) Neural Networks. Each LSTM layer is trained to produce the higher-level representations. We evaluate HRNN on real-world thyroid nodule ultrasound reports. The experiment results show that HRNN outperforms the baseline models with ultrasound reports.
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Acknowledgments
This work was supported by the Shanghai Innovation Action Project of Science and Technology (15511106900), the Science and Technology Development Foundation of Shanghai (16JC1400802), and the Shanghai Specific Fund Project for Information Development (XX-XXFZ-01-14-6349).
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Chen, D., Shi, C., Wang, M., Pan, Q. (2017). Thyroid Nodule Classification Using Hierarchical Recurrent Neural Network with Multiple Ultrasound Reports. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_77
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DOI: https://doi.org/10.1007/978-3-319-70139-4_77
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