Label-Preserving Data Augmentation in Latent Space for Diabetic Retinopathy Recognition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-43898-1_28
Reference26 articles.
1. Abdal, R., Qin, Y., Wonka, P.: Image2stylegan: how to embed images into the stylegan latent space? In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4432–4441 (2019)
2. Agustin, T., Utami, E., Al Fatta, H.: Implementation of data augmentation to improve performance CNN method for detecting diabetic retinopathy. In: 2020 3rd International Conference on Information and Communications Technology (ICOIACT), pp. 83–88. IEEE (2020)
3. Akram, U.M., Khan, S.A.: Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy. J. Med. Syst. 36, 3151–3162 (2012)
4. Alyoubi, W.L., Shalash, W.M., Abulkhair, M.F.: Diabetic retinopathy detection through deep learning techniques: a review. Inf. Med. Unlocked 20, 100377 (2020)
5. Araújo, T., et al.: Data augmentation for improving proliferative diabetic retinopathy detection in eye fundus images. IEEE Access 8, 182462–182474 (2020)
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