Search

Your search keyword '"Li, Xirong"' showing total 572 results
Start Over Author "Li, Xirong"

Refine your results

more »

more »

more »

more »
572 results on '"Li, Xirong"'

203. Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks

Author

Xu, Zhiyan, Wang, Weisen, Yang, Jingyuan, Zhao, Jianchun, Ding, Dayong, He, Feng, Chen, Di, Yang, Zhikun, Li, Xirong, Yu, Weihong, and Chen, Youxin

Abstract

AimsTo investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images.MethodsA retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset.ResultsOn a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen’s κ 0.828), exceeds slightly over the best expert (Human1, Cohen’s κ 0.810). For recognising PCV, the model outperformed the best expert as well.ConclusionA bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health.

Published
2021
Full Text
View/download PDF

210. Socializing the Semantic Gap

Author

Li, Xirong, primary, Uricchio, Tiberio, additional, Ballan, Lamberto, additional, Bertini, Marco, additional, Snoek, Cees G. M., additional, and Bimbo, Alberto Del, additional

Published
2016
Full Text
View/download PDF

214. Antidepressant Effects of Electroconvulsive Therapy Correlate With Subgenual Anterior Cingulate Activity and Connectivity in Depression

Author

Liu, Yi, primary, Du, Lian, additional, Li, Yongmei, additional, Liu, Haixia, additional, Zhao, Wenjing, additional, Liu, Dan, additional, Zeng, Jinkun, additional, Li, Xingbao, additional, Fu, Yixiao, additional, Qiu, Haitang, additional, Li, Xirong, additional, Qiu, Tian, additional, Hu, Hua, additional, Meng, Huaqing, additional, and Luo, Qinghua, additional

Published
2015
Full Text
View/download PDF

223. Tag relevance fusion for social image retrieval.

Author

Li, Xirong

Subjects
IMAGE retrieval, TAGS (Metadata), DIGITAL images, ONLINE social networks, INFORMATION retrieval
Abstract

Due to the subjective nature of social tagging, measuring the relevance of social tags with respect to the visual content is crucial for retrieving the increasing amounts of social-networked images. Witnessing the limit of a single measurement of tag relevance, we introduce in this paper tag relevance fusion as an extension to methods for tag relevance estimation. We present a systematic study, covering tag relevance fusion in early and late stages, and in supervised and unsupervised settings. Experiments on a large present-day benchmark set show that tag relevance fusion leads to better image retrieval. Moreover, unsupervised tag relevance fusion is found to be practically as effective as supervised tag relevance fusion, but without the need of any training efforts. This finding suggests the potential of tag relevance fusion for real-world deployment. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

225. Tag Relevance Fusion for Social Image Retrieval

Author

Li, Xirong and Li, Xirong

Abstract

Due to the subjective nature of social tagging, measuring the relevance of social tags with respect to the visual content is crucial for retrieving the increasing amounts of social-networked images. Witnessing the limit of a single measurement of tag relevance, we introduce in this paper tag relevance fusion as an extension to methods for tag relevance estimation. We present a systematic study, covering tag relevance fusion in early and late stages, and in supervised and unsupervised settings. Experiments on a large present-day benchmark set show that tag relevance fusion leads to better image retrieval. Moreover, unsupervised tag relevance fusion is found to be practically as effective as supervised tag relevance fusion, but without the need of any training efforts. This finding suggests the potential of tag relevance fusion for real-world deployment.

Published
2014
Full Text
View/download PDF

226. Adaptive Tag Selection for Image Annotation

Author

He, Xixi, Li, Xirong, Yang, Gang, Xu, Jieping, Jin, Qin, He, Xixi, Li, Xirong, Yang, Gang, Xu, Jieping, and Jin, Qin

Abstract

Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from the seen set. The effectiveness of the proposed method is justified by our participation in the ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground truth available for 207 tags, the benchmark evaluation shows that compared to the popular top-$k$ strategy which obtains an F-score of 0.122, adaptive tag selection achieves a higher F-score of 0.223. Moreover, by treating the underlying image annotation system as a black box, the new method can be used as an easy plug-in to boost the performance of existing systems.

Published
2014

232. SLC17A7 Gene May Be the Indicator of Selective Serotonin Reuptake Inhibitor Treatment Response in the Chinese Han Population

Author

Li, Xin, primary, Yu, Tao, additional, Huang, Xiaoye, additional, Cao, Yanfei, additional, Li, Xingwang, additional, Liu, Baocheng, additional, Yang, Fengping, additional, Li, Weidong, additional, Zhao, Xinzhi, additional, Feng, Guoyin, additional, Zhang, Xu, additional, Dong, Zaiquan, additional, Lin, Yi, additional, Li, Xirong, additional, He, Lin, additional, Sun, Xueli, additional, and He, Guang, additional

Published
2014
Full Text
View/download PDF

238. Numerical analysis of InxGa1−xN/SnS and AlxGa1−xN/SnS heterojunction solar cells.

Author

Lin, Shuo, Li, Xirong, Pan, Huaqing, Chen, Huanting, Li, Xiuyan, Li, Yan, and Zhou, Jinrong

Subjects
*SOLAR cell manufacturing, *PHOTOVOLTAIC power generation, *SOLAR cell efficiency, *INDIUM compounds, *COMPLEX compounds, *HETEROJUNCTIONS
Abstract

In this work the photovoltaic properties of In x Ga 1 − x N/SnS and Al x Ga 1 − x N/SnS heterojunction solar cells are studied by numerical analysis. The photovoltaic performances of In x Ga 1 − x N/SnS solar cells are enhanced with the decreasing In content and the GaN/SnS solar cell exhibits the highest efficiency. The efficiencies of GaN/SnS solar cell improve with the increased SnS thickness and the reduced GaN thickness. For the Al x Ga 1 − x N/SnS solar cells, there is electron barrier in the Al x Ga 1 − x N/SnS interface. The electron barrier becomes larger with increasing Al content and lead to the degraded efficiency of Al x Ga 1 − x N/SnS solar cells. The simulation contributes to designing and fabricating SnS solar cells. [ABSTRACT FROM AUTHOR]

Published
2016
Full Text
View/download PDF

240. Numerical analysis of SnS homojunction solar cell.

Author

Lin, Shuo, Li, Xirong, Pan, Huaqing, Chen, Huanting, Li, Xiuyan, Li, Yan, and Zhou, Jinrong

Subjects
*SOLAR cells, *TIN compounds, *SEMICONDUCTOR junctions, *PHOTOVOLTAIC cells, *THICKNESS measurement, *NUMERICAL analysis
Abstract

The photovoltaic properties of SnS homojunction solar cells are simulated with different parameters of the structure, including the doping concentration and the thickness of each layer. The simulated results show that the optimum efficiency of SnS homojunction solar cell is 25.268% with 5 × 10 15 cm −3 carrier concentration of the front region and 5 × 10 16 cm −3 carrier concentration of the base, a 50 nm thick n-layer and a 950 nm thick p-layer. The effects of minority-carrier lifetime and surface recombination velocities on the performance of solar cell have also been discussed. The simulation contributes to designing and fabricating SnS solar cells. [ABSTRACT FROM AUTHOR]

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results