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864 results on '"Zou Haidong"'

1. Cross-Fundus Transformer for Multi-modal Diabetic Retinopathy Grading with Cataract

Author

Xiao, Fan, Hou, Junlin, Zhao, Ruiwei, Feng, Rui, Zou, Haidong, Lu, Lina, Xu, Yi, and Zhang, Juzhao

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Diabetic retinopathy (DR) is a leading cause of blindness worldwide and a common complication of diabetes. As two different imaging tools for DR grading, color fundus photography (CFP) and infrared fundus photography (IFP) are highly-correlated and complementary in clinical applications. To the best of our knowledge, this is the first study that explores a novel multi-modal deep learning framework to fuse the information from CFP and IFP towards more accurate DR grading. Specifically, we construct a dual-stream architecture Cross-Fundus Transformer (CFT) to fuse the ViT-based features of two fundus image modalities. In particular, a meticulously engineered Cross-Fundus Attention (CFA) module is introduced to capture the correspondence between CFP and IFP images. Moreover, we adopt both the single-modality and multi-modality supervisions to maximize the overall performance for DR grading. Extensive experiments on a clinical dataset consisting of 1,713 pairs of multi-modal fundus images demonstrate the superiority of our proposed method. Our code will be released for public access., Comment: 10 pages, 4 figures

Published
2024

8. A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children

Author

Qi, Ziyi, Li, Tingyao, Chen, Jun, Yam, Jason C., Wen, Yang, Huang, Gengyou, Zhong, Hua, He, Mingguang, Zhu, Dan, Dai, Rongping, Qian, Bo, Wang, Jingjing, Qian, Chaoxu, Wang, Wei, Zheng, Yanfei, Zhang, Jian, Yi, Xianglong, Wang, Zheyuan, Zhang, Bo, Liu, Chunyu, Cheng, Tianyu, Yang, Xiaokang, Li, Jun, Pan, Yan-Ting, Ding, Xiaohu, Xiong, Ruilin, Wang, Yan, Zhou, Yan, Feng, Dagan, Liu, Sichen, Du, Linlin, Yang, Jinliuxing, Zhu, Zhuoting, Bi, Lei, Kim, Jinman, Tang, Fangyao, Zhang, Yuzhou, Zhang, Xiujuan, Zou, Haidong, Ang, Marcus, Tham, Clement C., Cheung, Carol Y., Pang, Chi Pui, Sheng, Bin, He, Xiangui, and Xu, Xun

Published
2024
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14. Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images

Author

Hou, Junlin, Xu, Jilan, Xiao, Fan, Zhao, Rui-Wei, Zhang, Yuejie, Zou, Haidong, Lu, Lina, Xue, Wenwen, and Feng, Rui

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally focus on single-field fundus images, which have limited field of view for precise eye examinations. In clinical applications, ophthalmologists adopt two-field fundus photography as the dominating tool, where the information from each field (i.e.,macula-centric and optic disc-centric) is highly correlated and complementary, and benefits comprehensive decisions. However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies. In this work, we first construct a new benchmark dataset (DRTiD) for DR grading, consisting of 3,100 two-field fundus images. To the best of our knowledge, it is the largest public DR dataset with diverse and high-quality two-field images. Then, we propose a novel DR grading approach, namely Cross-Field Transformer (CrossFiT), to capture the correspondence between two fields as well as the long-range spatial correlations within each field. Considering the inherent two-field geometric constraints, we particularly define aligned position embeddings to preserve relative consistent position in fundus. Besides, we perform masked cross-field attention during interaction to flter the noisy relations between fields. Extensive experiments on our DRTiD dataset and a public DeepDRiD dataset demonstrate the effectiveness of our CrossFiT network. The new dataset and the source code of CrossFiT will be publicly available at https://github.com/FDU-VTS/DRTiD., Comment: BIBM 2022

Published
2022

16. Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images

Author

Hou, Junlin, Xiao, Fan, Xu, Jilan, Zhang, Yuejie, Zou, Haidong, and Feng, Rui

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the segmentation of DR lesions task, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the image quality assessment task, we create an ensemble of InceptionV3, SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as well as the hybrid MixUp and CutMix strategy are both adopted to boost the generalization ability of our model. In the DR grading task, we build a Vision Transformer (ViT) and fnd that the ViT model pre-trained on color fundus images serves as a useful substrate for OCTA images. Our proposed methods ranked 4th, 3rd, and 5th on the three leaderboards of DRAC, respectively. The source code will be made available at https://github.com/FDU-VTS/DRAC.

Published
2022

17. Towards Label-Efficient Deep Learning for Myopic Maculopathy Classification

Author

Hou, Junlin, Xu, Jilan, Xiao, Fan, Zhang, Bo, Xu, Yiqian, Zhang, Yuejie, Zou, Haidong, Feng, Rui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Chen, Hao, editor, and Wong, Tien Yin, editor

Published
2024
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18. Ensemble Deep Learning Approaches for Myopic Maculopathy Plus Lesions Segmentation

Author

Xiao, Fan, Hou, Junlin, Xu, Jilan, Xu, Yiqian, Zhang, Bo, Zhang, Yuejie, Zou, Haidong, Feng, Rui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Chen, Hao, editor, and Wong, Tien Yin, editor

Published
2024
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26. Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images

Author

Li, Jiaxuan, Jin, Peiyao, Zhu, Jianfeng, Zou, Haidong, Xu, Xun, Tang, Min, Zhou, Minwen, Gan, Yu, He, Jiangnan, Ling, Yuye, and Su, Yikai

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we developed a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conducted experiments on human peripapillary retinal OCT images. The Dice score of the proposed segmentation network is 0.820$\pm$0.001 and the pixel accuracy is 0.830$\pm$0.002, both of which outperform those from other state-of-the-art techniques.

Published
2021

32. Application of Image Recognition in Equipment Monitoring

Author

Zou, Haidong, Yang, Shaoqiang, Wu, Wei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hung, Jason C., editor, Chang, Jia-Wei, editor, and Pei, Yan, editor

Published
2023
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33. Implementation and first-year screening results of an ocular telehealth system for diabetic retinopathy in China

Author

Xu Xun, Bai Xuelin, Shen Bingjie, Fu Jiong, Wang Weiwei, Zou Haidong, Peng Jinjuan, and Zhang Xi

Subjects
Public aspects of medicine, RA1-1270
Abstract

Abstract Background To describe implementation and first-year screening results of the first Chinese telehealth system for diabetic retinopathy (DR) - the Beixinjing Community Diabetic Retinopathy Telehealth system (BCDRT). Methods BCDRT implementation was based on the acquisition of adequate digital retinographs, secure digital transmission, storage and retrieval of participants' data and reader-generated medical reports. Local diabetic residents meeting inclusion criteria were enrolled into the BCDRT system beginning in 2009. Participants recommended for further in-person examination with ophthalmologists were followed, and the consistencies in diagnoses between BCDRT and ophthalmologists for DR or macular edema were calculated. Results A total of 471 diabetic residents participated in BCDRT screening in 2009. The proportions of total DR, proliferative DR, and diabetic macular edema were 24.42% (115 patients), 2.12% (10 patients) and 6.47% (24 patients), respectively: 56 patients consulted ophthalmologists for further in-person retinal examination with funduscopy after pupil dilation. High rates of consistency between BCDRT screening and ophthalmologists were observed for macular edema (Kappa = 0.81), moderate or severe non-proliferative DR grade (Kappa = 0.92), and other DR grades (Kappa = 1). A total of 456 (96.82%) patients were willing to participate in the next BCDRT screening. Conclusions BCDRT was a reliable and valid system for DR screening, and offers the potential to increase DR annual screening rates in local residents.

Published
2011
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34. Assessing the severity of conjunctivochalasis in a senile population: a community-based epidemiology study in Shanghai, China

Author

Zhang Guili, Zhou Huanming, Shi Caicai, Peng Jinjuan, Zou Haidong, Li Qingsong, Zhang Xingru, Xiang Minhong, and Li Yijie

Subjects
Public aspects of medicine, RA1-1270
Abstract

Abstract Background In a previous hospital-based study, conjunctivochalasis was diagnosed in 85.24% of 1416 patients aged 1 to 94 years and in 98.5% of patients aged 60 or older. This report is the first to present data from a large scale epidemiologic study on conjunctivochalasis in a community-based population, thereby helping to better identify the severity of conjunctivochalasis in the general population. Methods This community-based epidemiologic study was conducted to evaluate prevalence rates and related factors of conjunctivochalasis among people over 60 years old in the CaoYangXinCun community, Shanghai, China. Cluster sampling was used in randomly selected local residents aged no less than 60 years. A cross-sectional study using a slit-lamp ophthalmoscope for examination was carried out from September 2008 to October 2008. A modified grading system based on the well-accepted Meller and Tseng's system was used. SPSS10.0 software was used for data and statistical analysis. Results A total of 2110 residents took part in this study, with a response rate of 94.85%. Among these, 930 cases were confirmed as conjunctivochalasis, with a prevalence rate of 44.08%. The prevalence rate increased with age (X2 = 10.44, P < 0.01). A total of 1762 eyes were confirmed as conjunctivochalasis eyes. Of these eyes, 943 were classified as Grade I (53.52%), 647 as Grade II (36.72%), 162 as Grade III (9.19%), and the remaining 10 eyes as Grade IV (0.57%). The conjunctiva usually accumulated on the nasal and temporal areas of the conjunctival sac (944 eyes, 53.58%). Conclusions Although the rate estimated in the present study is not as high as reported in the former hospital-based study, conjunctivochalasis is considered a common age-related eye disease, deserving more attention to its early diagnosis.

Published
2011
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45. PM 2.5 Exposure as a Risk Factor for Optic Nerve Health in Type 2 Diabetes Mellitus.

Author

Yuan, Tianyi, Cheng, Minna, Ma, Yingyan, Zou, Haidong, Kan, Haidong, Meng, Xia, Guo, Yi, Peng, Ziwei, Xu, Yi, Lu, Lina, Ling, Saiguang, Dong, Zhou, Wang, Yuheng, Yang, Qinping, Xu, Wenli, Shi, Yan, Liu, Cong, and Lin, Senlin

Subjects
TYPE 2 diabetes, OPTIC disc, METEOROLOGICAL satellites, CENTRAL nervous system, OPTIC nerve
Abstract

(1) Objective: This study investigated the relationship between long-term particulate matter (PM2.5) exposure and optic disc parameters—vertical cup-to-disc ratio (vCDR), vertical optic disc diameter (vDD), and vertical optic cup diameter (vCD)—in patients with type 2 diabetes mellitus (T2DM). (2) Methods: A cross-sectional analysis was conducted using data from 65,750 T2DM patients in the 2017–2018 Shanghai Cohort Study of Diabetic Eye Disease (SCODE). Optic disc parameters were extracted from fundus images, and PM2.5 exposure was estimated using a random forest model incorporating satellite and meteorological data. Multivariate linear regression models were applied, adjusting for confounders including age, gender, body mass index, blood pressure, glucose, time of T2DM duration, smoking, drinking, and physical exercise. (3) Results: A 10 μg/m3 increase in PM2.5 exposure was associated with significant reductions in vCDR (−0.008), vDD (−42.547 μm), and vCD (−30.517 μm) (all p-values < 0.001). These associations persisted after sensitivity analyses and adjustments for other pollutants like O3 and NO2. (4) Conclusions: Long-term PM2.5 exposure is associated with detrimental changes in optic disc parameters in patients with T2DM, suggesting possible optic nerve atrophy. Considering the close relationship between the optic nerve and the central nervous system, these findings may also reflect broader neurodegenerative processes. [ABSTRACT FROM AUTHOR]

Published
2024
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