Abstract
Medical image classification (for example, lesions on MRI scans) is a very challenging task due to the complicated relationships between different lesion sub-types and expensive cost to collect high quality labelled training datasets. Graph model has been used to model the complicated relationship for medical imaging classification successfully in many previous work. However, most existing graph based models assumed the structure is known or pre-defined, and the classification performance severely depends on the pre-defined structure. To address all the problems of current graph learning models, we proposed to jointly learn the graph structure and use it for classification task in one framework. Besides imaging features, we also use the disease semantic features (learned from clinical reports), and predefined lymph node ontology graph to construct the graph structure. We evaluated our model on a T2 MRI image dataset with 821 samples and 14 types of lymph nodes. Although this dataset is very unbalanced on different types of lymph nodes, our model shows promising classification results on this challenging datasets compared to several state of art methods.
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Chen, J., Yang, J.: Robust subspace segmentation via low-rank representation. IEEE Trans. Cybern. 44(8), 1432–1445 (2014)
Franceschi, L., Niepert, M., Pontil, M., He, X.: Learning discrete structures for graph neural networks. In: Proceedings of the 36th International Conference on Machine Learning (2019)
Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: Methods and applications. CoRR abs/1709.05584 (2017)
Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2019)
Lu, J., Xu, C., Zhang, W., Duan, L.Y., Mei, T.: Sampling wisely: Deep image embedding by top-k precision optimization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019)
Sui, Y., Wang, G., Zhang, L.: Sparse subspace clustering via low-rank structure propagation. Pattern Recogn. 95, 261–271 (2019)
Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res. 6, 1453–1484 (2005)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. CoRR abs/1901.00596 (2019)
Yadati, N., Nimishakavi, M., Yadav, P., Louis, A., Talukdar, P.P.: Hypergcn: Hypergraph convolutional networks for semi-supervised classification. CoRR abs/1809.02589 (2018). http://arxiv.org/abs/1809.02589
Zareian, A., Karaman, S., Chang, S.: Bridging knowledge graphs to generate scene graphs. In: CVPR (2020)
Zhang, D., et al.: Knowledge graph-based image classification refinement. IEEE Access 7, 57678–57690 (2019)
Zhou, Y., Sun, Y., Honavar, V.G.: Improving image captioning by leveraging knowledge graphs. CoRR abs/1901.08942 (2019)
Zhu, X., Zhang, S., Li, Y., Zhang, J., Yang, L., Fang, Y.: Low-rank sparse subspace for spectral clustering. IEEE Trans. Knowl. Data Eng. 31(8), 1532–1543 (2019)
Zhu, Y., Zhu, X., Kim, M., Yan, J., Kaufer, D., Wu, G.: Dynamic hyper-graph inference framework for computer-assisted diagnosis of neurodegenerative diseases. IEEE Trans. Med. Imaging 38(2), 608–616 (2019)
Acknowledgment
This research was supported in part by the Intramural Research Program of the National Institutes of Health, Clinical Center and National Library of Medicine.
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Zhu, Y. et al. (2021). Learning Structure from Visual Semantic Features and Radiology Ontology for Lymph Node Classification on MRI. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_11
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DOI: https://doi.org/10.1007/978-3-030-87589-3_11
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