Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Mao-nian Wu"'
Autor:
Mao-Nian Wu, Kai He, Yi-Bei Yu, Bo Zheng, Shao-Jun Zhu, Xiang-Qian Hong, Wen-Qun Xi, Zhe Zhang
Publikováno v:
International Journal of Ophthalmology, Vol 17, Iss 7, Pp 1184-1192 (2024)
AIM: To evaluate the application of an intelligent diagnostic model for pterygium. METHODS: For intelligent diagnosis of pterygium, the attention mechanisms—SENet, ECANet, CBAM, and Self-Attention—were fused with the lightweight MobileNetV2 model
Externí odkaz:
https://doaj.org/article/38fdd209f8184daf8d4ee4b4e86aa04f
Autor:
Bang Chen, Xin-Wen Fang, Mao-Nian Wu, Shao-Jun Zhu, Bo Zheng, Bang-Quan Liu, Tao Wu, Xiang-Qian Hong, Jian-Tao Wang, Wei-Hua Yang
Publikováno v:
International Journal of Ophthalmology, Vol 16, Iss 9, Pp 1386-1394 (2023)
Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians
Externí odkaz:
https://doaj.org/article/3053296b6f27446d852819bf4eb52b64
Autor:
Shao-Jun Zhu, Hao-Dong Zhan, Mao-Nian Wu, Bo Zheng, Bang-Quan Liu, Shao-Chong Zhang, Wei-Hua Yang
Publikováno v:
International Journal of Ophthalmology, Vol 16, Iss 7, Pp 995-1004 (2023)
AIM: To conduct a classification study of high myopic maculopathy (HMM) using limited datasets, including tessellated fundus, diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and macular atrophy, and minimize annotation costs, and to opti
Externí odkaz:
https://doaj.org/article/9585f14be33548dab6fca5ab85c8d980
Publikováno v:
Guoji Yanke Zazhi, Vol 22, Iss 6, Pp 1016-1019 (2022)
AIM: To study the precise segmentation of pterygium lesions using the convolutional neural networks from artificial intelligence.METHODS: The network structure of Phase-fusion PSPNet for the segmentation of pterygium lesions is proposed based on the
Externí odkaz:
https://doaj.org/article/f270eee0476f43719c835b2e78c5c838
Publikováno v:
Guoji Yanke Zazhi, Vol 22, Iss 5, Pp 711-715 (2022)
AIM: To evaluate the application value of the automatic classification and diagnosis system of pterygium based on deep learning.METHODS: A total of 750 images of normal, observational and operative anterior sections of pterygium were collected from t
Externí odkaz:
https://doaj.org/article/eeac4f785a384e669bd887910c93b0dc
Autor:
Bo Zheng, Mao-nian Wu, Shao-jun Zhu, Hong-xia Zhou, Xiu-lan Hao, Fang-qin Fei, Yun Jia, Jian Wu, Wei-hua Yang, Xue-ping Pan
Publikováno v:
BMC Health Services Research, Vol 21, Iss 1, Pp 1-13 (2021)
Abstract Background In the development of artificial intelligence in ophthalmology, the ophthalmic AI-related recognition issues are prominent, but there is a lack of research into people’s familiarity with and their attitudes toward ophthalmic AI.
Externí odkaz:
https://doaj.org/article/dcde015a4c004a57861924ba9f0b426c
Publikováno v:
Frontiers in Psychology, Vol 12 (2021)
Objective: This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs.Methods: A total of 1,220 anterior segment phot
Externí odkaz:
https://doaj.org/article/6b8128684f5644da9a45745489b56d0a
Autor:
Ming Weng, Bo Zheng, Mao-Nian Wu, Shao-Jun Zhu, Yuan-Qiang Sun, Yun-Fang Liu, Zi-Wei Ma, Yun-Liang Jiang, Yong Liu, Wei-Hua Yang
Publikováno v:
Guoji Yanke Zazhi, Vol 18, Iss 3, Pp 568-571 (2018)
AIM: To evaluate a deep learning-assisted diagnostic system with an artificial intelligence for the detection of diabetic retinopathy(DR). METHODS:A total of 186 patients(372 eyes)with diabetes were recruited from January to July 2017. Discrepancies
Externí odkaz:
https://doaj.org/article/abf30c37e5044e4cb61b64d86930cbc5
Autor:
Zhu Shaojun, Chenghu Wang, Hao Xiulan, Kai He, Bo Zheng, Weihua Yang, Qin Jiang, Ling Jin, Liu Yunfang, Mao-Nian Wu
Publikováno v:
Disease Markers
Disease Markers, Vol 2021 (2021)
Disease Markers, Vol 2021 (2021)
Aims. The lack of primary ophthalmologists in China results in the inability of basic-level hospitals to diagnose pterygium patients. To solve this problem, an intelligent-assisted lightweight pterygium diagnosis model based on anterior segment image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a45751b532413c4f6e3caa5e7d5c4167
http://europepmc.org/articles/PMC8342163
http://europepmc.org/articles/PMC8342163
Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Autor:
Zhou Hongxia, Weihua Yang, Mao-Nian Wu, Bing Lu, Qin Jiang, Zhu Shaojun, Zheng Bo, Hao Xiulan, Kai He
Publikováno v:
Translational Vision Science & Technology
Purpose The discrepancy of the number between ophthalmologists and patients in China is large. Retinal vein occlusion (RVO), high myopia, glaucoma, and diabetic retinopathy (DR) are common fundus diseases. Therefore, in this study, a five-category in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::845390f2268ba6d00145ac5c4ba88357
https://doi.org/10.1167/tvst.10.7.20
https://doi.org/10.1167/tvst.10.7.20