Self-supervised category selective attention classifier network for diabetic macular edema classification
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00592-024-02257-6.pdf
Reference27 articles.
1. Iyer SS, Radhakrishnan NS, Roohipourmoallai R, Guerin CM, Maylath JS, Garson N (2023) Chronic ocular small vessel disease: An overview of diabetic retinopathy and its relationship with cardiovascular health. American Heart Journal Plus: Cardiology Research and Practice. 100270.
2. Semeraro F, Morescalchi F, Cancarini A, Russo A, Rezzola S, Costagliola C (2019) Diabetic retinopathy, a vascular and inflammatory disease: therapeutic implications. Diabetes Metab 45(6):517–527
3. Lanzetta P, Sarao V, Scanlon PH, Barratt J, Porta M, Bandello F, Loewenstein A (2020) Fundamental principles of an effective diabetic retinopathy screening program. Acta Diabetol 57:785–798
4. Liu X, Ali TK, Singh P, Shah A, McKinney SM, Ruamviboonsuk P, Turner AW, Keane PA, Chotcomwongse P, Nganthavee V, Chia M (2022) Deep learning to detect OCT-derived diabetic macular edema from color retinal photographs: a multicenter validation study. Ophthalmol Retina 6(5):398–410
5. Li X, Hu X, Yu L, Zhu L, Fu CW, Heng PA (2019) CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans Med Imaging 39(5):1483–1493
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