Zobrazeno 1 - 10
of 530
pro vyhledávání: '"Zhao, Yitian"'
Autor:
Liu, Shouyue, Hao, Jinkui, Liu, Yonghuai, Fu, Huazhu, Guo, Xinyu, Zhang, Shuting, Zhao, Yitian
Early detection of dementia, such as Alzheimer's disease (AD) or mild cognitive impairment (MCI), is essential to enable timely intervention and potential treatment. Accurate detection of AD/MCI is challenging due to the high complexity, cost, and of
Externí odkaz:
http://arxiv.org/abs/2408.05117
Autor:
Yu, Qinkai, Xie, Jianyang, Nguyen, Anh, Zhao, He, Zhang, Jiong, Fu, Huazhu, Zhao, Yitian, Zheng, Yalin, Meng, Yanda
Diabetic retinopathy (DR) is a complication of diabetes and usually takes decades to reach sight-threatening levels. Accurate and robust detection of DR severity is critical for the timely management and treatment of diabetes. However, most current D
Externí odkaz:
http://arxiv.org/abs/2407.04068
Autor:
Xie, Qihang, Guo, Mengguo, Mou, Lei, Zhang, Dan, Chen, Da, Shan, Caifeng, Zhao, Yitian, Su, Ruisheng, Zhang, Jiong
Cerebrovascular diseases (CVDs) remain a leading cause of global disability and mortality. Digital Subtraction Angiography (DSA) sequences, recognized as the golden standard for diagnosing CVDs, can clearly visualize the dynamic flow and reveal patho
Externí odkaz:
http://arxiv.org/abs/2406.00341
Autor:
Liu, Shouyue, Hao, Jinkui, Xu, Yanwu, Fu, Huazhu, Guo, Xinyu, Liu, Jiang, Zheng, Yalin, Liu, Yonghuai, Zhang, Jiong, Zhao, Yitian
Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image bioma
Externí odkaz:
http://arxiv.org/abs/2311.06009
Autor:
Hao, Jinkui, Shen, Ting, Zhu, Xueli, Liu, Yonghuai, Behera, Ardhendu, Zhang, Dan, Chen, Bang, Liu, Jiang, Zhang, Jiong, Zhao, Yitian
Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper,
Externí odkaz:
http://arxiv.org/abs/2208.10745
Autor:
Chinkamol, Amrest, Kanjaras, Vetit, Sawangjai, Phattarapong, Zhao, Yitian, Sudhawiyangkul, Thapanun, Chantrapornchai, Chantana, Guan, Cuntai, Wilaiprasitporn, Theerawit
While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the data annotation process on the curvilinear structure like the retinal
Externí odkaz:
http://arxiv.org/abs/2207.12238
Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolution is important for the quantification and analysis of retinal vasculature. However, the resolution of OCTA images is inversely proportional to the field of view at the same sa
Externí odkaz:
http://arxiv.org/abs/2207.11882
Autor:
Li, Heng, Liu, Haofeng, Fu, Huazhu, Shu, Hai, Zhao, Yitian, Luo, Xiaoling, Hu, Yan, Liu, Jiang
Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. However, for cataract patients, the fundus image always suffers quality degradation caused by the clouding lens. The degradation prevents reliable diagnos
Externí odkaz:
http://arxiv.org/abs/2206.04684
Autor:
Zhang, Hongrun, Meng, Yanda, Zhao, Yitian, Qiao, Yihong, Yang, Xiaoyun, Coupland, Sarah E., Zheng, Yalin
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs). However, MIL approaches for this specific classification problem still face unique challenges, particularly those related t
Externí odkaz:
http://arxiv.org/abs/2203.12081
Publikováno v:
IEEE Transactions on Medical Imaging,2022, 41(7), 1699-1710
Cataracts are the leading cause of vision loss worldwide. Restoration algorithms are developed to improve the readability of cataract fundus images in order to increase the certainty in diagnosis and treatment for cataract patients. Unfortunately, th
Externí odkaz:
http://arxiv.org/abs/2203.07737