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1,811 results on '"Bi, Lei"'

102. Underwater Target Tracking Algorithm Based on Optical Flow

Author

Yin, Xinyan, Wu, Song, Liu, Ying, Qin, Zihang, Bi, Lei, Fan, Ruifeng, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhang, Lin, editor, Yu, Wensheng, editor, Jiang, Haijun, editor, and Laili, Yuanjun, editor

Published
2022
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104. Hybrid Dual and Meet-LWE Attack

Author

Bi, Lei, Lu, Xianhui, Luo, Junjie, Wang, Kunpeng, 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, Nguyen, Khoa, editor, Yang, Guomin, editor, Guo, Fuchun, editor, and Susilo, Willy, editor

Published
2022
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105. Detecting Temporal Pain Status of Postoperative Children from Facial Expression

Author

Wu, Wenhao, Bi, Lei, Ren, Weihong, Nie, Wei, Lin, Ruihan, Liu, Zuode, Li, Sufang, Lin, Xi, Liu, Honghai, 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, Liu, Honghai, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Jiang, Li, editor, Gu, Guoying, editor, Wu, Xinyu, editor, and Ren, Weihong, editor

Published
2022
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106. Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer

Author

Meng, Mingyuan, Peng, Yige, Bi, Lei, Kim, Jinman, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2022
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113. Increased Cerebral Level of P2X7R in a Tauopathy Mouse Model by PET Using [18F]GSK1482160

Author

Kong, Yanyan, primary, Cao, Lei, additional, Wang, Jiao, additional, Zhuang, Junyi, additional, Liu, Yongshan, additional, Bi, Lei, additional, Qiu, Yifan, additional, Hou, Yuyi, additional, Huang, Qi, additional, Xie, Fang, additional, Yang, Yunhao, additional, Shi, Kuangyu, additional, Rominger, Axel, additional, Guan, Yihui, additional, Jin, Hongjun, additional, and Ni, Ruiqing, additional

Published
2024
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129. Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation

Author

Bi, Lei, Feng, Dagan, and Kim, Jinman

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis. In recent years, segmentation methods based on fully convolutional networks (FCN) have achieved great success in general images. This success is primarily due to the leveraging of large labelled datasets to learn features that correspond to the shallow appearance as well as the deep semantics of the images. However, the dependence on large dataset does not translate well into medical images. To improve the FCN performance for skin lesion segmentations, researchers attempted to use specific cost functions or add post-processing algorithms to refine the coarse boundaries of the FCN results. However, the performance of these methods is heavily reliant on the tuning of many parameters and post-processing techniques. In this paper, we leverage the state-of-the-art image feature learning method of generative adversarial network (GAN) for its inherent ability to produce consistent and realistic image features by using deep neural networks and adversarial learning concept. We improve upon GAN such that skin lesion features can be learned at different level of complexities, in a controlled manner. The outputs from our method is then augmented to the existing FCN training data, thus increasing the overall feature diversity. We evaluated our method on the ISIC 2018 skin lesion segmentation challenge dataset and showed that it was more accurate and robust when compared to the existing skin lesion segmentation methods., Comment: 6 pages

Published
2018

144. Nanophotonic devices based on magneto-optical materials: recent developments and applications

Author

Qin Jun, Xia Shuang, Yang Weihao, Wang Hanbing, Yan Wei, Yang Yucong, Wei Zixuan, Liu Wenen, Luo Yi, Deng Longjiang, and Bi Lei

Subjects
all-dielectric resonator, biosensor, chiromagnetic metasurface, magnetic field sensing, magneto-optical effects, magnetoplasmonics, Physics, QC1-999
Abstract

Interaction between light and magnetism in magneto-optical (MO) nanophotonic devices has been actively studied in the past few years. The recent development of MO all-dielectric resonators and metasurfaces has led to the emergence of various novel MO phenomena that were not observed in their bulk counterparts. For example, a large s-polarized transverse MO Kerr effect can be observed at magnetic resonance wavelength, which cannot exist in the bare MO films. We review recent developments in nanophotonic devices based on MO materials and focus on different modes and related MO effects in nanophotonic structures with emphasis on recently discovered new MO phenomena in magnetoplasmonics and all-dielectric nanostructures, such as dark mode, all-dielectric Mie resonance and waveguide mode. Further, we discuss the potential applications of these nanostructures for biological/chemical sensing, magnetic field sensing, and magnetic field-controlled active and nonreciprocal metasurfaces.

Published
2022
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145. A Classification Network for Ocular Diseases Based on Structure Feature and Visual Attention

Author

Wen, Yang, Xu, Yupeng, Liu, Kun, Sheng, Bin, Bi, Lei, Kim, Jinman, He, Xiangui, Xu, Xun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Magnenat-Thalmann, Nadia, editor, Interrante, Victoria, editor, Thalmann, Daniel, editor, Papagiannakis, George, editor, Sheng, Bin, editor, Kim, Jinman, editor, and Gavrilova, Marina, editor

Published
2021
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146. Multi-Stream Fusion Network for Multi-Distortion Image Super-Resolution

Author

Wen, Yang, Xu, Yupeng, Sheng, Bin, Li, Ping, Bi, Lei, Kim, Jinman, He, Xiangui, Xu, Xun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Magnenat-Thalmann, Nadia, editor, Interrante, Victoria, editor, Thalmann, Daniel, editor, Papagiannakis, George, editor, Sheng, Bin, editor, Kim, Jinman, editor, and Gavrilova, Marina, editor

Published
2021
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147. Predicting the Concrete Security of LWE Against the Dual Attack Using Binary Search

Author

Li, Shuaigang, Lu, Xianhui, Zhang, Jiang, Li, Bao, Bi, Lei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gao, Debin, editor, Li, Qi, editor, Guan, Xiaohong, editor, and Liao, Xiaofeng, editor

Published
2021
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150. Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

Author

Bi, Lei, Kim, Jinman, Kumar, Ashnil, Feng, Dagan, and Fulham, Michael

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods., Comment: 9 pages, 2 figures

Published
2017

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