Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Guo, Dazhou"'
Autor:
Yu, Qinji, Wang, Yirui, Yan, Ke, Li, Haoshen, Guo, Dazhou, Zhang, Li, Lu, Le, Shen, Na, Wang, Qifeng, Ding, Xiaowei, Ye, Xianghua, Jin, Dakai
Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for cancer diagnosis, staging, and treatment planning. Finding scatteredly
Externí odkaz:
http://arxiv.org/abs/2404.03819
Autor:
Guo, Heng, Zhang, Jianfeng, Huang, Jiaxing, Mok, Tony C. W., Guo, Dazhou, Yan, Ke, Lu, Le, Jin, Dakai, Xu, Minfeng
Segment anything model (SAM) demonstrates strong generalization ability on natural image segmentation. However, its direct adaption in medical image segmentation tasks shows significant performance drops with inferior accuracy and unstable results. I
Externí odkaz:
http://arxiv.org/abs/2403.15063
Autor:
Yan, Ke, Jin, Dakai, Guo, Dazhou, Xu, Minfeng, Shen, Na, Hua, Xian-Sheng, Ye, Xianghua, Lu, Le
Finding abnormal lymph nodes in radiological images is highly important for various medical tasks such as cancer metastasis staging and radiotherapy planning. Lymph nodes (LNs) are small glands scattered throughout the body. They are grouped or defin
Externí odkaz:
http://arxiv.org/abs/2307.15271
Autor:
Wang, Puyang, Guo, Dazhou, Zheng, Dandan, Zhang, Minghui, Yu, Haogang, Sun, Xin, Ge, Jia, Gu, Yun, Lu, Le, Ye, Xianghua, Jin, Dakai
Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthma and lung cancer. Unlike other organs with simpler shapes or topology
Externí odkaz:
http://arxiv.org/abs/2306.09116
Autor:
Zhang, Minghui, Wu, Yangqian, Zhang, Hanxiao, Qin, Yulei, Zheng, Hao, Tang, Wen, Arnold, Corey, Pei, Chenhao, Yu, Pengxin, Nan, Yang, Yang, Guang, Walsh, Simon, Marshall, Dominic C., Komorowski, Matthieu, Wang, Puyang, Guo, Dazhou, Jin, Dakai, Wu, Ya'nan, Zhao, Shuiqing, Chang, Runsheng, Zhang, Boyu, Lv, Xing, Qayyum, Abdul, Mazher, Moona, Su, Qi, Wu, Yonghuang, Liu, Ying'ao, Zhu, Yufei, Yang, Jiancheng, Pakzad, Ashkan, Rangelov, Bojidar, Estepar, Raul San Jose, Espinosa, Carlos Cano, Sun, Jiayuan, Yang, Guang-Zhong, Gu, Yun
Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image r
Externí odkaz:
http://arxiv.org/abs/2303.05745
Autor:
Ji, Zhanghexuan, Guo, Dazhou, Wang, Puyang, Yan, Ke, Lu, Le, Xu, Minfeng, Zhou, Jingren, Wang, Qifeng, Ge, Jia, Gao, Mingchen, Ye, Xianghua, Jin, Dakai
Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new incremental segmentation clas
Externí odkaz:
http://arxiv.org/abs/2302.00162
Autor:
Li, Zihan, Li, Yunxiang, Li, Qingde, Wang, Puyang, Guo, Dazhou, Lu, Le, Jin, Dakai, Zhang, You, Hong, Qingqi
Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the
Externí odkaz:
http://arxiv.org/abs/2206.14718
Autor:
Guo, Dazhou, Ge, Jia, Ye, Xianghua, Yan, Senxiang, Xin, Yi, Song, Yuchen, Huang, Bing-shen, Hung, Tsung-Min, Zhu, Zhuotun, Peng, Ling, Ren, Yanping, Liu, Rui, Zhang, Gong, Mao, Mengyuan, Chen, Xiaohua, Lu, Zhongjie, Li, Wenxiang, Chen, Yuzhen, Huang, Lingyun, Xiao, Jing, Harrison, Adam P., Lu, Le, Lin, Chien-Yu, Jin, Dakai, Ho, Tsung-Ying
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable prohibitive la
Externí odkaz:
http://arxiv.org/abs/2111.01544
Autor:
Ye, Xianghua, Guo, Dazhou, Tseng, Chen-kan, Ge, Jia, Hung, Tsung-Min, Pai, Ping-Ching, Ren, Yanping, Zheng, Lu, Zhu, Xinli, Peng, Ling, Chen, Ying, Chen, Xiaohua, Chou, Chen-Yu, Chen, Danni, Yu, Jiaze, Chen, Yuzhen, Jiao, Feiran, Xin, Yi, Huang, Lingyun, Xie, Guotong, Xiao, Jing, Lu, Le, Yan, Senxiang, Jin, Dakai, Ho, Tsung-Ying
Background: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation of high labor-costs and interuser variability. Purpose: To validate the clinical applicability of a deep learning (DL) multi-mod
Externí odkaz:
http://arxiv.org/abs/2110.05280
Autor:
Liu, Fengze, Yan, Ke, Harrison, Adam, Guo, Dazhou, Lu, Le, Yuille, Alan, Huang, Lingyun, Xie, Guotong, Xiao, Jing, Ye, Xianghua, Jin, Dakai
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm SAM, which is capable of computing dense anatomical/semantic correspondences between two images at t
Externí odkaz:
http://arxiv.org/abs/2109.11572