Wangyi Fang

Unsupervised domain adaptation model for lesion detection in retinal OCT images | Ophthalmology | Chinese Academy of Sciences Background and objective Optical Coherence Tomography (OCT) is one of the most used retinal imaging modalities in the clinic as it can provide high-resolution anatomical images. The huge number of OCT images has significantly advanced the development of deep learning methods for automatic lesion detection to ease the doctor's workload.

X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, and C. Zheng, “A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT,” IEEE Trans. Med. Imaging 39(8), 2615–2625 (2020). [Crossref] X. Ma, Z. Ji, S. Niu, T. Leng, D. L. Rubin, and Q. Chen, “MS-CAM: Multi-scale class activation maps for weakly-supervised segmentation of geographic atrophy lesions in SD-OCT images,” IEEE J. Biomed. Health Inform. 24(12), 3443–3455 (2020). [Crossref] D. Romo-Bucheli, P. Seeböck, J. I. Orlando, B. S.

ORIGINAL ARTICLE Yizhen Tang candytyz@gmail.com orcid.org/0000-0002-1516-7044 Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, China Search for more papers by this author Wangyi Fang orcid.org/0000-0002-9824-8009 Eye Institute and Department of...