Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Shi, Danli"'
Autor:
Zhang, Weiyi, Yang, Jiancheng, Chen, Ruoyu, Huang, Siyu, Xu, Pusheng, Chen, Xiaolan, Lu, Shanfu, Cao, Hongyu, He, Mingguang, Shi, Danli
Fundus fluorescein angiography (FFA) is crucial for diagnosing and monitoring retinal vascular issues but is limited by its invasive nature and restricted accessibility compared to color fundus (CF) imaging. Existing methods that convert CF images to
Externí odkaz:
http://arxiv.org/abs/2410.13242
Autor:
Shi, Danli, Zhang, Weiyi, Yang, Jiancheng, Huang, Siyu, Chen, Xiaolan, Yusufu, Mayinuer, Jin, Kai, Lin, Shan, Liu, Shunming, Zhang, Qing, He, Mingguang
Early detection of eye diseases like glaucoma, macular degeneration, and diabetic retinopathy is crucial for preventing vision loss. While artificial intelligence (AI) foundation models hold significant promise for addressing these challenges, existi
Externí odkaz:
http://arxiv.org/abs/2409.06644
Autor:
Zhang, Weiyi, Huang, Siyu, Yang, Jiancheng, Chen, Ruoyu, Ge, Zongyuan, Zheng, Yingfeng, Shi, Danli, He, Mingguang
Fundus Fluorescein Angiography (FFA) is a critical tool for assessing retinal vascular dynamics and aiding in the diagnosis of eye diseases. However, its invasive nature and less accessibility compared to Color Fundus (CF) images pose significant cha
Externí odkaz:
http://arxiv.org/abs/2408.15217
Ultrawide-field fluorescein angiography (UWF-FA) facilitates diabetic retinopathy (DR) detection by providing a clear visualization of peripheral retinal lesions. However, the intravenous dye injection with potential risks hamper its application. We
Externí odkaz:
http://arxiv.org/abs/2408.10636
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
Human-in-the-loop (HITL) strategy has been recently introduced into the field of medical image processing. Indocyanine green angiography (ICGA) stands as a well-established examination for visualizing choroidal vasculature and detecting chorioretinal
Externí odkaz:
http://arxiv.org/abs/2406.01993
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
Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine. In the medical domain, LLMs hold promise for tasks ranging from clinical decision support to patient
Externí odkaz:
http://arxiv.org/abs/2405.07468
Autor:
Chen, Xiaolan, Zhao, Ziwei, Zhang, Weiyi, Xu, Pusheng, Gao, Le, Xu, Mingpu, Wu, Yue, Li, Yinwen, Shi, Danli, He, Mingguang
Artificial intelligence (AI) has gained significant attention in healthcare consultation due to its potential to improve clinical workflow and enhance medical communication. However, owing to the complex nature of medical information, large language
Externí odkaz:
http://arxiv.org/abs/2403.00840
Recent studies have validated the association between cardiovascular disease (CVD) risk and retinal fundus images. Combining deep learning (DL) and portable fundus cameras will enable CVD risk estimation in various scenarios and improve healthcare de
Externí odkaz:
http://arxiv.org/abs/2206.09202
Autor:
Jiang, Yuzhe, Chen, Qi, Shi, Danli, Miao, Suyu, Liu, Yifeng, Wang, Jinyang, Liu, Lin, Chen, Yufan, Wang, Ruobing
Publikováno v:
In Multiple Sclerosis and Related Disorders August 2024 88