Abstract
Recently, an increasing of deep learning models has been introduced to address various fundus image tasks, e.g. segmentation, classification, and enhancement. Concurrently, this emergence has been accompanied by the development of increasingly intricate model architectures and a parameter surge. However, these methods have overlooked the crucial significance of the intrinsic characteristics of the data. In this regard, we posit that a vanilla ResNet, when coupled with appropriate data augmentation, can also achieve a favorable performance. This study assesses the performance of the foundational deep learning architecture, the standard Residual Network (ResNet), in predicting Spherical Equivalent (SE) values. Spherical Equivalent (SE) is a crucial parameter in optometry employed for prescribing eyeglasses to correct vision. Our study intends to demonstrate the effectiveness of ResNet with proper data augmentations in achieving precise SE predictions without additional enhancements. Utilizing a vanilla ResNet50 model, we secured the third position in the Myopic Maculopathy Analysis Challenge 2023, task 3. Remarkably, our findings reveal that the unaltered deployment of ResNet yields exceptional predictive performance for estimating SE, highlighting the architectural prowess of simple models in a vital yet frequently demanding optometric context. Our software package is available at https://github.com/HuayuLiArizona/Prediction-of-Spherical-Equivalent-With-Vanilla-ResNet.
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References
Arega, T.W., Legrand, F., Bricq, S., Meriaudeau, F.: Using MRI-specific data augmentation to enhance the segmentation of right ventricle in multi-disease, multi-center and multi-view cardiac MRI. In: Puyol Anton, E., et al. (eds.) Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. Lecture Notes in Computer Science(), vol. 13131, pp. 250–258. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-93722-5_27
Charng, J., Alam, K., Swartz, G., Kugelman, J., Alonso-Caneiro, D., Mackey, D.A., Chen, F.K.: Deep learning: applications in retinal and optic nerve diseases. Clin. Exp. Optom. 106(5), 466–475 (2023)
Dai, L., Wu, L., Li, H., Cai, C., Wu, Q., Kong, H., Liu, R., Wang, X., Hou, X., Liu, Y., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 3242 (2021)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint: arXiv:2010.11929 (2020)
Enaholo, E.S., Musa, M.J., Zeppieri, M.: The spherical equivalent. In: StatPearls [Internet]. StatPearls Publishing (2023)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
Heo, B., et al.: AdamP: slowing down the slowdown for momentum optimizers on scale-invariant weights. arXiv preprint: arXiv:2006.08217 (2020)
Holden, B.A., et al.: Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050. Ophthalmology 123(5), 1036–1042 (2016)
Ikuno, Y.: Overview of the complications of high myopia. Retina 37(12), 2347–2351 (2017)
Li, X., Hu, X., Yu, L., Zhu, L., Fu, C.W., Heng, P.A.: CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans. Med. Imaging 39, 1483–1493 (2020)
Liu, R., et al.: DeepDRiD: diabetic retinopathy-grading and image quality estimation challenge. Patterns 3(6) (2022)
Liu, Z., et al.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)
Ohno-Matsui, K., et al.: International photographic classification and grading system for myopic maculopathy. Am. J. Ophthalmol. 159(5), 877–883 (2015)
Silva, R.: Myopic maculopathy: a review. Ophthalmologica 228(4), 197–213 (2012)
Singh, A., Jothi Balaji, J., Rasheed, M.A., Jayakumar, V., Raman, R., Lakshminarayanan, V.: Evaluation of explainable deep learning methods for ophthalmic diagnosis. Clin. Ophthalmol., 2573–2581 (2021)
Sun, R., Li, Y., Zhang, T., Mao, Z., Wu, F., Zhang, Y.: Lesion-aware transformers for diabetic retinopathy grading. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10938–10947 (2021)
Uysal, E.S., Bilici, M.Ş., Zaza, B.S., Özgenç, M.Y., Boyar, O.: Exploring the limits of data augmentation for retinal vessel segmentation. arXiv preprint: arXiv:2105.09365 (2021)
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., Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2017. Lecture Notes in Computer Science(), vol. 10435, pp. 267–275. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_31
Yokoi, T., Ohno-Matsui, K.: Diagnosis and treatment of myopic maculopathy. Asia-Pac. J. Ophthalmol. 7(6), 415–421 (2018)
Yu, S., et al.: MIL-VT: multiple instance learning enhanced vision transformer for fundus image classification. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2021. Lecture Notes in Computer Science(), vol. 12908, pp. 45–54. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_5
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint: arXiv:1710.09412 (2017)
Zhou, Y., et al.: Collaborative learning of semi-supervised segmentation and classification for medical images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Zhu, W., et al.: Self-supervised equivariant regularization reconciles multiple instance learning: Joint referable diabetic retinopathy classification and lesion segmentation. In: 18th International Symposium on Medical Information Processing and Analysis (SIPAIM) (2022)
Zhu, W., et al.: OTRE: where optimal transport guided unpaired image-to-image translation meets regularization by enhancing. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds.) Information Processing in Medical Imaging. Lecture Notes in Computer Science, vol. 13939, pp. 415–427. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34048-2_32
Zhu, W., Qiu, P., Farazi, M., Nandakumar, K., Dumitrascu, O.M., Wang, Y.: Optimal transport guided unsupervised learning for enhancing low-quality retinal images. arXiv preprint: arXiv:2302.02991 (2023)
Zhu, W., Qiu, P., Lepore, N., Dumitrascu, O.M., Wang, Y.: NNMobile-Net: rethinking CNN design for deep learning-based retinopathy research. arXiv preprint: arXiv:2306.01289 (2023)
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Li, H., Zhu, W., Chen, X., Wang, Y. (2024). Prediction of Spherical Equivalent with Vanilla ResNet. In: Sheng, B., Chen, H., Wong, T.Y. (eds) Myopic Maculopathy Analysis. MICCAI 2023. Lecture Notes in Computer Science, vol 14563. Springer, Cham. https://doi.org/10.1007/978-3-031-54857-4_6
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