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100 results on '"Li, Bicao"'

4. CDBIFusion: A Cross-Domain Bidirectional Interaction Fusion Network for PET and MRI Images

Author

Zhang, Jie, Li, Bicao, Wang, Bei, Shao, Zhuhong, Huang, Jie, Lu, Jiaxi, 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, Qingshan, editor, Wang, Hanzi, editor, Ma, Zhanyu, editor, Zheng, Weishi, editor, Zha, Hongbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Ji, Rongrong, editor

Published
2024
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11. PSR-Net: A Dual-Branch Pyramid Semantic Reasoning Network for Segmentation of Remote Sensing Images

Author

Wang, Lijun, Li, Bicao, Wang, Bei, Li, Chunlei, Huang, Jie, Song, Mengxing, 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, Iliadis, Lazaros, editor, Papaleonidas, Antonios, editor, Angelov, Plamen, editor, and Jayne, Chrisina, editor

Published
2023
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13. CTCNet: A Bi-directional Cascaded Segmentation Network Combining Transformers with CNNs for Skin Lesions

Author

Wang, Jing, Li, Bicao, Guo, Xuwei, Huang, Jie, Song, Mengxing, Wei, Miaomiao, 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, Yu, Shiqi, editor, Zhang, Zhaoxiang, editor, Yuen, Pong C., editor, Han, Junwei, editor, Tan, Tieniu, editor, Guo, Yike, editor, Lai, Jianhuang, editor, and Zhang, Jianguo, editor

Published
2022
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14. DMF-CL: Dense Multi-scale Feature Contrastive Learning for Semantic Segmentation of Remote-Sensing Images

Author

Song, Mengxing, Li, Bicao, Wei, Pingjun, Shao, Zhuhong, Wang, Jing, Huang, Jie, 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, Yu, Shiqi, editor, Zhang, Zhaoxiang, editor, Yuen, Pong C., editor, Han, Junwei, editor, Tan, Tieniu, editor, Guo, Yike, editor, Lai, Jianhuang, editor, and Zhang, Jianguo, editor

Published
2022
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20. Self-attention Deep Saliency Network for Fabric Defect Detection

Author

Wang, Jinjin, Liu, Zhoufeng, Li, Chunlei, Yang, Ruimin, Li, Bicao, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pan, Linqiang, editor, Liang, Jing, editor, and Qu, Boyang, editor

Published
2020
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21. Fast and High-Purity Seed Sorting Method Based on Lightweight CNN

Author

Luan, Zhengguang, Li, Chunlei, Ding, Shumin, Guo, Qiang, Li, Bicao, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pan, Linqiang, editor, Liang, Jing, editor, and Qu, Boyang, editor

Published
2020
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34. HyperTDP-Net: A Hyper-densely Connected Compression-and-Decomposition Network Based on Trident Dilated Perception for PET and MRI Image Fusion.

Author

Li, Bicao, Du, Yifan, Wang, Bei, Shao, Zhuhong, Huang, Jie, Wei, Miaomiao, and Lu, Jiaxi

Subjects
*IMAGE fusion, *POSITRON emission tomography, *MAGNETIC resonance imaging, *COMPUTER-assisted image analysis (Medicine), *DIAGNOSTIC imaging, *TECHNOLOGICAL innovations, *MULTISPECTRAL imaging, *MEDICAL equipment
Abstract

Objective. Since medical images generated by medical devices have low spatial resolution and quality, fusion approaches on medical images can generate a fused image containing a more comprehensive range of different modal features to help physicians accurately diagnose diseases. Conventional methods based on deep learning for medical image fusion usually extract only local features without considering their global features, which often leads to the problem of unclear detail information in the final fused image. Therefore, medical image fusion is a challenging task of great relevance. Approach. This paper proposes a novel end-to-end medical image fusion model for PET and MRI images to achieve information interaction between different pathways, termed as hyper-densely connected compression-and-decomposition network based on trident dilated perception for PET and MRI image fusion (HyperTDP-Net). In particular, in the compression network, a dual residual hyper densely module is constructed to take full advantage of middle layer information. Moreover, we establish the trident dilated perception module to precisely determine the location information of features, and improve the feature representation capability of the network. In addition, we abandon the ordinary mean square error as the content loss function and propose a new content-aware loss consisting of structural similarity loss and gradient loss, so that the fused image not only contains rich texture details but also maintains sufficient structural similarity with the source images. Main results. The experimental dataset used in this paper is derived from multimodal medical images published by Harvard Medical School. Extensive experiments illustrate that our model contains more edge information and texture detail information in the fusion result than the 12 state-of-the-art fusion models and ablation study results demonstrate the effectiveness of three technical innovations. Significance. As medical images continue to be used in clinical diagnosis, our method is expected to be a tool that can effectively improve the accuracy of physician diagnosis and automatic machine detection. [ABSTRACT FROM AUTHOR]

Published
2023
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