Abstract
Macular edema is the most important cause of visual impairment in the center of human eyes, which causes a lot of life problems for a large number of patients. Optical coherence tomography is a very important medical imaging material in the diagnosis and treatment of macular diseases. Firstly, on the basis of Faster R-CNN, this paper adjusts the processing strategy of the model by modifying the tag generation method to detect the lesions area of OCT images of fundus lesions. Then, using the U-Net basic model, the task of semantics segmentation of OCT images of fundus lesions is accomplished by fusing multi-attention modules in the decoding stage. Good results have been achieved in the OCT medical image dataset of the largest fundus lesions, which can help doctors quickly identify and locate the lesions areas in the image, and quantify the severity of specific fundus edema.
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References
Kim, B.-C., Sung, Y.S., Suk, H.-I.: Deep feature learning for pulmonary nodule classification in a lung CT. In: 2016 4th International Winter Conference on Brain-Computer Interface (BCI). IEEE (2016)
Venhuizen, F.G., van Ginneken, B., Liefers, B., et al.: Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks. Biomed. Opt. Express 8(7), 3292–3316 (2017)
Zhou, L., Yu, Q., Xu, X., et al.: Improving dense conditional random field for retinal vessel segmentation by discriminative feature learning and thin-vessel enhancement. Comput. Methods Programs Biomed. 148, 13–25 (2017)
Kermany, D.S., Goldbaum, M., Cai, W., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)
Girshick, R.: Fast R-CNN. In: The IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Zhao, B., et al.: Diversified visual attention networks for fine-grained object classification. IEEE Trans. Multimed. 19(6), 1245–1256 (2017)
Wang, F., et al.: Residual attention network for image classification (2017). arXiv preprint arXiv:1704.06904
Wang, F., Jiang, M., Qian, C., et al.: Residual attention network for image classification (2017). arXiv preprint arXiv:1704.06904
Acknowledgement
This work is supported by the National Science Foundation of China (No. U1633103), the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (No. CAAC-ITRB-201502).
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Li, Z., Zhao, X., Yin, A., Guo, C., Chen, L. (2019). Research on Deep Learning-Based Intelligent Diagnosis Algorithms for OCT Medical Images of Macular Edema. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_67
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DOI: https://doi.org/10.1007/978-3-030-36808-1_67
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