Abstract
With the rapidly growing number of people affected by various retinal diseases, there is a strong clinical interest for fully automatic and accurate retinal disease recognition. The unique characteristics of how retinal diseases are manifested on the fundus images pose a major challenge for automatic recognition. In order to tackle the challenges, we propose a local-global dual perception (LGDP) based deep multiple instance learning (MIL) framework that integrates the instance contribution from both local scale and global scale. The major components of the proposed framework include a local pyramid perception module (LPPM) that emphasizes the key instances from the local scale, and a global perception module (GPM) that provides a spatial weight distribution from a global scale. Extensive experiments on three major retinal disease benchmarks demonstrate that the proposed framework outperforms many state-of-the-art deep MIL methods, especially for recognizing the pathological images. Last but not least, the proposed deep MIL framework can be conveniently embedded into any convolutional backbones via a plug-and-play manner and effectively boost the performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bourne, R., et al.: Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob. Health 5(9), e888–e897 (2017)
Ting, D., et al.: Deep learning in ophthalmology: the technical and clinical considerations. Prog. Retinal Eye Res. 72, 100759 (2019)
Abràmoff, M., Garvin, M., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)
Yau, J., et al.: Global prevalence and major risk factors of diabetic retinopathy. Diab. Care 35(3), 556–564 (2012)
Tham, Y., Li, X., Wong, T., Quigley, H., Aung, T., Cheng, C.: Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11), 2081–2090 (2014)
Wong, W., et al.: Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob. Health 2(2), e106–e116 (2014)
Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316(22), 2402–2410 (2016)
Ting, D., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. J. Am. Med. Assoc. 318(22), 2211–2223 (2017)
Phene, S., et al.: Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology 126(12), 1627–1639 (2019)
Liu, H., et al.: Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs. JAMA Ophthalmol. 137(12), 1353–1360 (2019)
Burlina, P., Joshi, N., Pekala, M., Pacheco, K., Freund, D., Bressler, N.: Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135(11), 1170–1176 (2017)
Grassmann, F., et al.: A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125(6), 1410–1420 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representation (2015)
Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Zhang, M., Li, J., Ji, W., Piao, Y., Lu, H.: Memory-oriented decoder for light field salient object detection. In: Advances in Neural Information Processing Systems, pp. 898–908 (2019)
Ji, W., et al.: Learning calibrated medical image segmentation via multi-rater agreement modeling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 12341–12351 (2021)
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, vol. 80, pp. 2127–2136 (2018)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42, 145–175 (2001)
Dietterich, T., Lathrop, R., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1), 31–71 (1997)
Zhang, M., Zhou, Z.: Improve multi-instance neural networks through feature selection. Neural Process. Lett. 19(1), 1–10 (2004)
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems (2003)
Bi, Q., Qin, K., Zhang, H., Li, Z., Xu, K., Xia, G.: A multiple-instance densely-connected ConvNet for aerial scene classification. IEEE Trans. Image Process. 29, 4911–4926 (2020)
Li, S., et al.: Multi-instance multi-scale CNN for medical image classification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 531–539. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_58
APTOS 2019 Blindness Detection (2019). https://www.kaggle.com/c/aptos2019-blindness-detection/data
Li, L., Xu, M., Wang, X., Jiang, L., Liu, H.: Attention based glaucoma detection: a large-scale database and CNN model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 10571–10580 (2019)
Wang, X., Yan, Y., Peng, T., Xiang, B., Liu, W.: Revisiting multiple instance neural networks. Pattern Recogn. 74, 15–24 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Acknowledgment
This work was funded by Key-Area Research and Development Program of Guangdong Province, China (No. 2018B010111001), and Scientific and Technical Innovation 2030 - ‘New Generation Artificial Intelligence’ Project (No.2020AAA0104100).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bi, Q. et al. (2021). Local-Global Dual Perception Based Deep Multiple Instance Learning for Retinal Disease Classification. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-87237-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87236-6
Online ISBN: 978-3-030-87237-3
eBook Packages: Computer ScienceComputer Science (R0)
