Zobrazeno 1 - 10
of 813
pro vyhledávání: '"Tham, Yih‐chung"'
Autor:
Wang, Meng, Lin, Tian, Lin, Aidi, Yu, Kai, Peng, Yuanyuan, Wang, Lianyu, Chen, Cheng, Zou, Ke, Liang, Huiyu, Chen, Man, Yao, Xue, Zhang, Meiqin, Huang, Binwei, Zheng, Chaoxin, Zhang, Peixin, Chen, Wei, Luo, Yilong, Chen, Yifan, Xia, Honghe, Shi, Tingkun, Zhang, Qi, Guo, Jinming, Chen, Xiaolin, Wang, Jingcheng, Tham, Yih Chung, Liu, Dianbo, Wong, Wendy, Thakur, Sahil, Fenner, Beau, Fang, Danqi, Liu, Siying, Liu, Qingyun, Huang, Yuqiang, Zeng, Hongqiang, Meng, Yanda, Zhou, Yukun, Jiang, Zehua, Qiu, Minghui, Zhang, Changqing, Chen, Xinjian, Wang, Sophia Y, Lee, Cecilia S, Sobrin, Lucia, Cheung, Carol Y, Pang, Chi Pui, Keane, Pearse A, Cheng, Ching-Yu, Chen, Haoyu, Fu, Huazhu
Previous foundation models for retinal images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases. To RetiZero's pr
Externí odkaz:
http://arxiv.org/abs/2406.09317
Autor:
Shi, Danli, Zhang, Weiyi, Chen, Xiaolan, Liu, Yexin, Yang, Jiancheng, Huang, Siyu, Tham, Yih Chung, Zheng, Yingfeng, He, Mingguang
Artificial intelligence (AI) is vital in ophthalmology, tackling tasks like diagnosis, classification, and visual question answering (VQA). However, existing AI models in this domain often require extensive annotation and are task-specific, limiting
Externí odkaz:
http://arxiv.org/abs/2405.11338
Autor:
Hou, Qingshan, Cheng, Shuai, Cao, Peng, Yang, Jinzhu, Liu, Xiaoli, Zaiane, Osmar R., Tham, Yih Chung
Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great ch
Externí odkaz:
http://arxiv.org/abs/2404.04887
Autor:
Zheng, Yingfeng, Wang, Wei, Zhong, Yuxin, Wu, Fengchun, Zhu, Zhuoting, Tham, Yih-Chung, Lamoureux, Ecosse, Xiao, Liang, Zhu, Erta, Liu, Haoning, Jin, Ling, Liang, Linyi, Luo, Lixia, He, Mingguang, Morgan, Ian, Congdon, Nathan, Liu, Yizhi
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 4, p e24316 (2021)
BackgroundThe COVID-19 pandemic has led to worldwide school closures, with millions of children confined to online learning at home. As a result, children may be susceptible to anxiety and digital eye strain, highlighting a need for population interv
Externí odkaz:
https://doaj.org/article/fe63d26caac943a7a7d7c3f4d755ee85
Autor:
Wong, Mark Yu Zheng, Gunasekeran, Dinesh Visva, Nusinovici, Simon, Sabanayagam, Charumathi, Yeo, Khung Keong, Cheng, Ching-Yu, Tham, Yih-Chung
Publikováno v:
JMIR Public Health and Surveillance, Vol 7, Iss 2, p e24445 (2021)
BackgroundThe COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown. ObjectiveWe used an infodemiological approach to estimate the wo
Externí odkaz:
https://doaj.org/article/ab16fef490ac45dd873dc53fcf59cc07
Autor:
Qiu, Jianing, Wu, Jian, Wei, Hao, Shi, Peilun, Zhang, Minqing, Sun, Yunyun, Li, Lin, Liu, Hanruo, Liu, Hongyi, Hou, Simeng, Zhao, Yuyang, Shi, Xuehui, Xian, Junfang, Qu, Xiaoxia, Zhu, Sirui, Pan, Lijie, Chen, Xiaoniao, Zhang, Xiaojia, Jiang, Shuai, Wang, Kebing, Yang, Chenlong, Chen, Mingqiang, Fan, Sujie, Hu, Jianhua, Lv, Aiguo, Miao, Hui, Guo, Li, Zhang, Shujun, Pei, Cheng, Fan, Xiaojuan, Lei, Jianqin, Wei, Ting, Duan, Junguo, Liu, Chun, Xia, Xiaobo, Xiong, Siqi, Li, Junhong, Lo, Benny, Tham, Yih Chung, Wong, Tien Yin, Wang, Ningli, Yuan, Wu
We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging devices, and demography. After pre-training, VisionFM provides a found
Externí odkaz:
http://arxiv.org/abs/2310.04992
Autor:
Lei, Xiaofeng, Li, Shaohua, Xu, Xinxing, Fu, Huazhu, Liu, Yong, Tham, Yih-Chung, Feng, Yangqin, Tan, Mingrui, Xu, Yanyu, Goh, Jocelyn Hui Lin, Goh, Rick Siow Mong, Cheng, Ching-Yu
Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elusive and easily confused with the background, and thus precise localiza
Externí odkaz:
http://arxiv.org/abs/2210.02445
Autor:
Nusinovici, Simon, Zhou, Lei, Wang, Xinyue, Tham, Yih Chung, Wang, Xiaomeng, Wong, Tien Yin, Chakravarthy, Usha, Cheng, Ching-Yu
Publikováno v:
In Ophthalmology Science September-October 2024 4(5)
Autor:
Sheng, Bin *, Pushpanathan, Krithi *, Guan, Zhouyu *, Lim, Quan Hziung *, Lim, Zhi Wei, Yew, Samantha Min Er, Goh, Jocelyn Hui Lin, Bee, Yong Mong, Sabanayagam, Charumathi, Sevdalis, Nick, Lim, Cynthia Ciwei, Lim, Chwee Teck, Shaw, Jonathan, Jia, Weiping, Ekinci, Elif Ilhan, Simó, Rafael, Lim, Lee-Ling †, Li, Huating †, *, Tham, Yih-Chung †, **
Publikováno v:
In The Lancet Diabetes & Endocrinology August 2024 12(8):569-595
Autor:
Yew, Samantha Min Er, Chen, Yibing, Goh, Jocelyn Hui Lin, Chen, David Ziyou, Chun Jin Tan, Marcus, Cheng, Ching-Yu, Teck Chang Koh, Victor, Tham, Yih Chung
Publikováno v:
In Advances in Ophthalmology Practice and Research August-September 2024 4(3):164-172