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2,993 results on '"Xie, Hai"'

3. A Novel Diffusion Model with Wavelet Transform for Optic Disc and Cup Segmentation in Fundus Images

Author

Dong, Xiang, Xie, Hai, Li, Li, Yang, Bao, Wang, Tianfu, Lei, Baiying, Goos, Gerhard, Series 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, Lin, Zhouchen, editor, Cheng, Ming-Ming, editor, He, Ran, editor, Ubul, Kurban, editor, Silamu, Wushouer, editor, Zha, Hongbin, editor, Zhou, Jie, editor, and Liu, Cheng-Lin, editor

Published
2025
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15. DiffDGSS: Generalizable Retinal Image Segmentation with Deterministic Representation from Diffusion Models

Author

Xie, Yingpeng, Qu, Junlong, Xie, Hai, Wang, Tianfu, Lei, Baiying, Goos, Gerhard, Series 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, Linguraru, Marius George, editor, Dou, Qi, editor, Feragen, Aasa, editor, Giannarou, Stamatia, editor, Glocker, Ben, editor, Lekadir, Karim, editor, and Schnabel, Julia A., editor

Published
2024
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16. A Super Token Vision Transformer and CNN Parallel Branch Network for mCNV Lesion Segmentation in OCT Images

Author

Dong, Xiang, Xie, Hai, Sun, Yunlong, Wu, Zhenquan, Yang, Bao, Qu, Junlong, Zhang, Guoming, Lei, Baiying, 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, Cao, Xiaohuan, editor, Xu, Xuanang, editor, Rekik, Islem, editor, Cui, Zhiming, editor, and Ouyang, Xi, editor

Published
2024
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22. ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

Author

Fang, Huihui, Li, Fei, Fu, Huazhu, Sun, Xu, Cao, Xingxing, Lin, Fengbin, Son, Jaemin, Kim, Sunho, Quellec, Gwenole, Matta, Sarah, Shankaranarayana, Sharath M, Chen, Yi-Ting, Wang, Chuen-heng, Shah, Nisarg A., Lee, Chia-Yen, Hsu, Chih-Chung, Xie, Hai, Lei, Baiying, Baid, Ujjwal, Innani, Shubham, Dang, Kang, Shi, Wenxiu, Kamble, Ravi, Singhal, Nitin, Wang, Ching-Wei, Lo, Shih-Chang, Orlando, José Ignacio, Bogunović, Hrvoje, Zhang, Xiulan, Xu, Yanwu, and group, iChallenge-AMD study

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

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models., Comment: 31 pages, 17 figures

Published
2022
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30. AIWW: a new nutrition-screening tool for the oncologic population

Author

Ge, Yi-Zhong, Fu, Zhen-Ming, Zhang, Qi, Song, Meng-Meng, Ruan, Guo-Tian, Zhang, Xi, Zhang, Xiao-Wei, Li, Xiang-Rui, Zhang, Kang-Ping, Tang, Meng, Liu, Xiao-Yue, Yang, Ming, Liu, Tong, Xie, Hai-Lun, Zhang, He-Yang, Wang, Zi-Wen, Hu, Chun-Lei, Lin, Shi-Qi, Zhang, Rui, Xu, Hong-Xia, Li, Wei, Song, Chun-Hua, Liu, Ming, Chen, Jun-Qiang, Wang, Kun-Hua, Bo, Li, Cong, Ming-Hua, Li, Zeng-Ning, Guo, Zeng-Qin, Wang, Xiao-Bin, Wang, Bin-Yan, Xu, Benjamin, Qin, Xian-Hui, Xu, Xi-Ping, Barazzoni, Rocco, Yao, Qing-Hua, Weng, Min, Shen, Xian, and Shi, Han-Ping

Published
2023
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34. Primary Surgery Followed by Selective Chemoradiotherapy Versus Preoperative Chemoradiotherapy Followed by Surgery for Locally Advanced Rectal Cancer: A Randomized Clinical Trial

Author

Li, Jun, Hu, Ye-Ting, Liu, Cheng-Cheng, Wang, Liu-Hong, Ju, Hai-Xing, Huang, Xue-Feng, Chi, Pan, Du, Jin-Lin, Wang, Jian-Ping, Xiao, Yi, Lin, Guo-Le, Zhang, Wei, Zhao, Hong, Liu, Ming, Song, Yong-Mao, Xu, Dong, Wang, Jian-Wei, Sun, Li-Feng, Xie, Hai-Ting, Cao, Hong-Feng, Xiao, Qian, Wang, Jian, Wu, Qing-Bin, Li, De-Chuan, Dai, Sheng, Jiang, Wei-Zhong, Shen, Li, Yuan, Ying, Wang, Zi-Qiang, and Ding, Ke-Feng

Published
2024
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42. Comprehensive prognostic effects of systemic inflammation and Insulin resistance in women with breast cancer with different BMI: a prospective multicenter cohort

Author

Ruan, Guo-Tian, Xie, Hai-Lun, Hu, Chun-Lei, Liu, Chen-An, Zhang, He-Yang, Zhang, Qi, Wang, Zi-Wen, Zhang, Xi, Ge, Yi-Zhong, Lin, Shi-Qi, Tang, Meng, Song, Meng-Meng, Zhang, Xiao-Wei, Liu, Xiao-Yue, Zhang, Kang-Ping, Yang, Ming, Yu, Kai-Ying, Wang, Kun-Hua, Hu, Wen, Deng, Li, Cong, Ming-Hua, and Shi, Han-Ping

Published
2023
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48. JefiGPU: Jefimenko's Equations on GPU

Author

Zhang, Jun-Jie, Chen, Jian-Nan, Peng, Guo-Liang, Du, Tai-Jiao, and Xie, Hai-Yan

Subjects
Physics - Computational Physics, Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Phenomenology, Physics - Geophysics
Abstract

We have implemented a GPU version of the Jefimenko's equations -- JefiGPU. Given the proper distributions of the source terms $\rho$ (charge density) and $\mathbf{J}$ (current density) in the source volume, the algorithm gives the electromagnetic fields in the observational region (not necessarily overlaps the vicinity of the sources). To verify the accuracy of the GPU implementation, we have compared the obtained results with that of the theoretical ones. Our results show that the deviations of the GPU results from the theoretical ones are around 5\%. Meanwhile, we have also compared the performance of the GPU implementation with a CPU version. The simulation results indicate that the GPU code is significantly faster than the CPU version. Finally, we have studied the parameter dependence of the execution time and memory consumption on one NVIDIA Tesla V100 card. Our code can be consistently coupled to RBG (Relativistic Boltzmann equations on GPUs) and many other GPU-based algorithms in physics., Comment: 21 pages, 8 figures, 4 tables

Published
2021
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49. Two typical collective behaviors of the heavy ions expanding in cold plasma with ambient magnetic field

Author

Peng, Guo-Liang, Zhang, Jun-Jie, Chen, Jian-Nan, Du, Tai-Jiao, and Xie, Hai-Yan

Subjects
Physics - Plasma Physics, Physics - Geophysics
Abstract

We have numerically studied the evolution of the heavy ions that expand in a cold background plasma at a large scale. Two typical collective behaviors of the heavy ions are identified with the conditions where only the traversing heavy ion's initial total mass is different. Our work has demonstrated that a difference in the initial total mass of the moving heavy ions is able to induce completely different collective behaviors of the plasma. The simulation is performed via the hybrid model, in which the ions and electrons are treated as classical particles and mass-less fluid, respectively. Due to the imbalance of the electric and magnetic force on the heavy ions, these particles will evolve into different collective patterns at the later time. These patterns manifest a rather different stopping behavior of the moving ions and an opposite drifting direction of the electron fluid at the rim of the expanding plasma. Further numerical and analytical calculations show that the imbalance depends not only on the number densities of the plasma ions, but also on the spatial variations of the magnetic fields. Our work reveals that the collective behavior of the heavy ions is highly non-linear, and the non-linearity is able to induce different phenomena in the evolution of the system at a large scale., Comment: 8 pages, 8 figures

Published
2021
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