Abstract
Disease grading and lesion identification are two important tasks for diabetic retinopathy detection. Disease grading uses image-level annotation but lesion identification often needs the fine-grained annotations, which requires a lot of time and effort of professional doctors. Therefore, it is a great challenge to complete disease grading and lesion identification simultaneously with the limited labeled data. We propose a method based on weakly supervised object localization and knowledge driven attribute mining to conduct disease grading and lesion identification using only image-level annotation. We first propose an Attention-Drop-Highlight Layer (ADHL), which enables the CNN to accurately and comprehensively focus on the various lesion features. Then, we design a search space and employ neural architecture search (NAS) to select the best settings of the ADHL, to maximize the performance of the model. Finally, we regard the lesion attributes corresponding to different disease grades as weakly supervised classification labels representing prior knowledge, and propose an Attribute Mining (AM) method to further improve the effect of disease grading and complete lesion identification. Extensive experiments and a user study have proved that our method can capture more lesion features, improve the performance of disease grading, and obtain state-of-the-art results compared to the methods only using image-level annotation.
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References
Kaggle diabetic retinopathy detection competition. https://www.kaggle.com/c/diabetic-retinopathy-detection
Bajwa, M.N., Taniguchi, Y., Malik, M.I., Neumeier, W., Dengel, A., Ahmed, S.: Combining fine- and coarse-grained classifiers for diabetic retinopathy detection. In: Zheng, Y., Williams, B.M., Chen, K. (eds.) MIUA 2019. CCIS, vol. 1065, pp. 242–253. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39343-4_21
Choe, J., Shim, H.: Attention-based dropout layer for weakly supervised object localization. In: CVPR, pp. 2219–2228 (2019)
Decencière, E., et al.: Feedback on a publicly distributed image database: the Messidor database. Image Anal. Stereol. 33(3), 231–234 (2014)
He, A., Li, T., Li, N., Wang, K., Fu, H.: Cabnet: category attention block for imbalanced diabetic retinopathy grading. IEEE Trans. Med. Imaging 40, 143–153 (2020)
Lin, Z., et al.: A framework for identifying diabetic retinopathy based on anti-noise detection and attention-based fusion. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 74–82. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_9
Sánchez, C.I., Niemeijer, M., Dumitrescu, A.V., Suttorp-Schulten, M.S., Abramoff, M.D., van Ginneken, B.: Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data. Invest. Ophthalmol. Vis. Sci. 52(7), 4866–4871 (2011)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV, pp. 618–626 (2017)
Vo, H.H., Verma, A.: New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color space. In: 2016 IEEE International Symposium on Multimedia (ISM), pp. 209–215. IEEE (2016)
Wang, X., et al.: Unifying structure analysis and surrogate-driven function regression for glaucoma OCT image screening. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 39–47. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_5
Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., Wang, X.: Zoom-in-net: deep mining lesions for diabetic retinopathy detection. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 267–275. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_31
Xing, X., et al.: Dynamic spectral graph convolution networks with assistant task training for early MCI diagnosis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 639–646. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_70
Zhang, C.L., Cao, Y.H., Wu, J.: Rethinking the route towards weakly supervised object localization. In: CVPR, pp. 13460–13469 (2020)
Zhou, Y., et al.: Collaborative learning of semi-supervised segmentation and classification for medical images. In: CVPR, pp. 2079–2088 (2019)
Zhou, Y., Wang, B., Huang, L., Cui, S., Shao, L.: A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability. IEEE Trans. Med. Imaging 40(3), 818–828 (2020)
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: CVPR, pp. 8697–8710 (2018)
Acknowledgments
This research is supported by National Key R&D Program of China (No. 2018YFC0115102), National Natural Science Foundation of China (Nos. 61872020, U20A20195), Beijing Natural Science Foundation Haidian Primitive Innovation Joint Fund (L182016), Beijing Advanced Innovation Center for Biomedical Engineering (ZF138G1714), Research Unit of Virtual Human and Virtual Surgery, Chinese Academy of Medical Sciences (2019RU004), Shenzhen Research Institute of Big Data, Shenzhen, 518000, and Global Visiting Fellowship of Bournemouth University.
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Wang, X., Gu, Y., Pan, J., Jia, L. (2021). Diabetic Retinopathy Detection Based on Weakly Supervised Object Localization and Knowledge Driven Attribute Mining. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2021. Lecture Notes in Computer Science(), vol 12970. Springer, Cham. https://doi.org/10.1007/978-3-030-87000-3_4
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