The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Prediction of Recurrence Probability of Thyroid Cancer Patients using Similarity Loss based Multi-modal Autoencoder
Abstract
Data used for the diagnosis of thyroid cancer patients include Thyroid Stimulating hormone (TSH), Thyroglobulin (Tg), Tg Antibodies (TgAb), and pathology information. However, existing thyroid cancer-related studies using machine learning tended to use a single type of data in model. Therefore, this study proposes a new model and method that can use all type of data together. The proposed method uses an autoencoder model composed of multiple encoders to extract comprehensive features from three types of hormone related data and pathology data and connects them to a classification layer to train them to be classified. As a result of the experiment, the proposed model using both types of hormonal and pathological data showed a performance improvement of up to about 58.1 times compared to the model using only a single type of data.
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“Some interesting examples include Kim and Shin (2023) that proposed a model employing an autoencoder with multiple encoders to extract comprehensive features from hormonal and pathological data and demonstrated that the proposed model significantly improves performance when compared to unimodal models when predicting the recurrence probability of thyroid cancer patients.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Kim and Shin, 2023
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016) · arXiv · doi:10.48550/arxiv.1607.00148 · Cited by 819
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Schouten, D., Nicoletti, G., Dille, B., Chia, C., Vendittelli, P., Schuurmans, M., Litjens, G., & Khalili, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103621 · Cited by 162
Metadata
- Authors
- Kim, J., & Shin, H.
- Affiliations
- Ajou University,Dept. Artificial Intelligence,Suwon,Korea
- Ajou University (KR)
- ORCID
- Shin, Hyunjung
- Venue
- 2023 IEEE International Conference on Big Data and Smart Computing (BigComp)
- Publisher
- IEEE
- Published
- 2023-02
- Pages
- 358-360
- OpenAlex
- W4327928758
- Cited by
- 1
- References
- 2
- Citations by year
- 2025: 1
- FWCI
- 2.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Big Data and Smart Computing (BigComp) (publishedVersion)
- Funders
- Ministry of Education
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Thyroid Cancer Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- thyroid cancer, tumor recurrence prediction, similarity loss, thyroid-stimulating hormone, thyroglobulin, thyroglobulin antibodies, feature extraction
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- CoChat. (2026). Citation check: Prediction of Recurrence Probability of Thyroid Cancer Patients using Similarity Loss based Multi-modal Autoencoder. https://app.cochat.ai/papers/10.1109/bigcomp57234.2023.00082
- The paper
- Kim, J., & Shin, H. (2023). Prediction of Recurrence Probability of Thyroid Cancer Patients using Similarity Loss based Multi-modal Autoencoder. 2023 IEEE International Conference on Big Data and Smart Computing (BigComp). https://doi.org/10.1109/bigcomp57234.2023.00082
