Zobrazeno 1 - 10
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pro vyhledávání: '"Kang, Ming"'
Brain tumor detection in multiplane Magnetic Resonance Imaging (MRI) slices is a challenging task due to the various appearances and relationships in the structure of the multiplane images. In this paper, we propose a new You Only Look Once (YOLO)-ba
Externí odkaz:
http://arxiv.org/abs/2410.21822
Existing brain tumor segmentation methods usually utilize multiple Magnetic Resonance Imaging (MRI) modalities in brain tumor images for segmentation, which can achieve better segmentation performance. However, in clinical applications, some modaliti
Externí odkaz:
http://arxiv.org/abs/2404.14019
Autor:
Yang, Ziyuan, Kang, Ming, Teoh, Andrew Beng Jin, Gao, Chengrui, Chen, Wen, Zhang, Bob, Zhang, Yi
In recent years, palmprints have been widely used for individual verification. The rich privacy information in palmprint data necessitates its protection to ensure security and privacy without sacrificing system performance. Existing systems often us
Externí odkaz:
http://arxiv.org/abs/2403.02680
Publikováno v:
In ICIP (2024) 2970--2974
Medical image semantic segmentation techniques can help identify tumors automatically from computed tomography (CT) scans. In this paper, we propose a Contextual and Attentional feature Fusions enhanced Convolutional Neural Network (CNN) and Transfor
Externí odkaz:
http://arxiv.org/abs/2401.16886
Autor:
Hu, Pei-Jin, Chen, Qi-Ling, Chen, Tian-Lu, Kang, Ming-Ming, Guo, Yi-Qing, Luo-Bu, Dan-Zeng, Feng, You-Liang, Gao, Qi, Gou, Quan-Bu, Hu, Hong-Bo, Li, Hai-Jin, Liu, Cheng, Liu, Mao-Yuan, Liu, Wei, Qian, Xiang-Li, Qiao, Bing-Qiang, Su, Jing-Jing, Sun, Hui-Ying, Wang, Xu, Wang, Zhen, Xin, Guang-Guang, Yang, Chao-Wen, Yao, Yu-Hua, Yuan, Qiang, Zhang, Yi
The detection of GW170817/GRB170817A implied the strong association between short gamma-ray bursts (SGRBs) and binary neutron star (BNS) mergers which produce gravitational waves (GWs). More evidence is needed to confirm the association and reveal th
Externí odkaz:
http://arxiv.org/abs/2401.11399
Publikováno v:
Image Vis. Comput. 147 (2024) 105057
We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO) framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell instance segmentation. Built on the YOLO segmentation framework, we employ
Externí odkaz:
http://arxiv.org/abs/2312.06458
Autor:
Cheng, Ao-Yan, Cai, Hao, Chen, Shi, Chen, Tian-Lu, Dong, Xiang, Feng, You-Liang, Gao, Qi, Gou, Quan-Bu, Guo, Yi-Qing, Hu, Hong-Bo, Kang, Ming-Ming, Li, Hai-Jin, Liu, Chen, Liu, Mao-Yuan, Liu, Wei, Min, Fang-Sheng, Pan, Chu-Cheng, Qiao, Bing-Qiang, Qian, Xiang-Li, Sun, Hui-Ying, Sun, Yu-Chang, Wang, Ao-Bo, Wang, Xu, Wang, Zhen, Xin, Guang-Guang, Yao, Yu-Hua, Yuan, Qiang, Zhang, Yi
The HADAR experiment, which will be constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high sensitivity advantages of focused Cherenkov detectors. Its physics objective is to observe transient
Externí odkaz:
http://arxiv.org/abs/2310.00343
Publikováno v:
In MICCAI (2024) 15008 35-45
You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection. In this paper, we develop a novel BGF-YOLO architecture by incorporating Bi-level routing attention, Generalized feature pyramid netw
Externí odkaz:
http://arxiv.org/abs/2309.12585
Publikováno v:
In MICCAI 2023 LNCS vol. 14223 600-610 (2023)
With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection. However, the performance of using YOLO networks is scarcely investigated in brain tumor detectio
Externí odkaz:
http://arxiv.org/abs/2307.16412
Publikováno v:
In ICIP (2024) 3024--3029
Blood cell detection is a typical small-scale object detection problem in computer vision. In this paper, we propose a CST-YOLO model for blood cell detection based on YOLOv7 architecture and enhance it with the CNN-Swin Transformer (CST), which is a
Externí odkaz:
http://arxiv.org/abs/2306.14590