FRE-GAN : Full-resolution efficient convolutional generative adversarial network for retinal vessel segmentation.
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| Title: | FRE-GAN : Full-resolution efficient convolutional generative adversarial network for retinal vessel segmentation. |
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| Authors: | Yu YF; Department of Statistics, Guangzhou University, Guangzhou, 510006, China. Electronic address: yuyufeng220@163.com., Yi H; Department of Statistics, Guangzhou University, Guangzhou, 510006, China. Electronic address: yihong3@mail2.sysu.edu.cn., Xu J; Zhuhai Clinical Medical College of Jinan University, 519050, China. Electronic address: xjianjun0828@163.com., Ding W; School of Information Science and Technology, Nantong University, Nantong, 226019, China; Faculty of Data Science, City University of Macau, Macau, 999078, China. Electronic address: ding.wp@ntu.edu.cn., Zhang C; School of Computer Science and Software, Zhaoqing University, Zhaoqing, 526061, China. Electronic address: chuanbinzhang@hotmail.com. |
| Source: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Oct; Vol. 202, pp. 109044. Date of Electronic Publication: 2026 Apr 28. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.109044 |
