Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Zhou Xiao-Yun"'
Autor:
Chen, Yu, Cao, Xu, Lin, Xiaoyi, Huang, Baoru, Zhou, Xiao-Yun, Zheng, Jian-Qing, Yang, Guang-Zhong
Accurate motion and depth recovery is important for many robot vision tasks including autonomous driving. Most previous studies have achieved cooperative multi-task interaction via either pre-defined loss functions or cross-domain prediction. This pa
Externí odkaz:
http://arxiv.org/abs/2208.11993
Autor:
Wang, Peng, Wu, Yuhsuan, Lai, Bolin, Zhou, Xiao-Yun, Lu, Le, Liu, Wendi, Zhou, Huabang, Huang, Lingyun, Xiao, Jing, Harrison, Adam P., Jia, Ningyang, Hu, Heping
Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation. Methods: we enrolled 400 patients who had either liver resection or a biopsy and was diagnosed with eithe
Externí odkaz:
http://arxiv.org/abs/2110.08817
Autor:
Cao, Xu, Chen, Zijie, Lai, Bolin, Wang, Yuxuan, Chen, Yu, Cao, Zhengqing, Yang, Zhilin, Ye, Nanyang, Zhao, Junbo, Zhou, Xiao-Yun, Qi, Peng
Venipucture is a common step in clinical scenarios, and is with highly practical value to be automated with robotics. Nowadays, only a few on-shelf robotic systems are developed, however, they can not fulfill practical usage due to varied reasons. In
Externí odkaz:
http://arxiv.org/abs/2105.12951
Autor:
Chen, Yu, Wang, Yuxuan, Lai, Bolin, Chen, Zijie, Cao, Xu, Ye, Nanyang, Ren, Zhongyuan, Zhao, Junbo, Zhou, Xiao-Yun, Qi, Peng
In the modern medical care, venipuncture is an indispensable procedure for both diagnosis and treatment. In this paper, unlike existing solutions that fully or partially rely on professional assistance, we propose VeniBot -- a compact robotic system
Externí odkaz:
http://arxiv.org/abs/2105.12945
Autor:
Zhou, Xiao-Yun, Lai, Bolin, Li, Weijian, Wang, Yirui, Zheng, Kang, Wang, Fakai, Lin, Chihung, Lu, Le, Huang, Lingyun, Han, Mei, Xie, Guotong, Xiao, Jing, Chang-Fu, Kuo, Harrison, Adam, Miao, Shun
Landmark localization plays an important role in medical image analysis. Learning based methods, including CNN and GCN, have demonstrated the state-of-the-art performance. However, most of these methods are fully-supervised and heavily rely on manual
Externí odkaz:
http://arxiv.org/abs/2104.14629
Autor:
Lai, Bolin, Wu, Yuhsuan, Zhou, Xiao-Yun, Wang, Peng, Lu, Le, Huang, Lingyun, Han, Mei, Xiao, Jing, Hu, Heping, Harrison, Adam P.
Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability. However, the high costs of annotations hinder the collection of large and completely
Externí odkaz:
http://arxiv.org/abs/2103.12972
Autor:
Wang, Yirui, Zheng, Kang, Chang, Chi-Tung, Zhou, Xiao-Yun, Zheng, Zhilin, Huang, Lingyun, Xiao, Jing, Lu, Le, Liao, Chien-Hung, Miao, Shun
Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohibitively high labor costs involved in large-scale medical image annotat
Externí odkaz:
http://arxiv.org/abs/2012.15359
Despite the empirical success in various domains, it has been revealed that deep neural networks are vulnerable to maliciously perturbed input data that much degrade their performance. This is known as adversarial attacks. To counter adversarial atta
Externí odkaz:
http://arxiv.org/abs/2012.08112
Autor:
Lai, Bolin, Wu, Yuhsuan, Bai, Xiaoyu, Zhou, Xiao-Yun, Wang, Peng, Cai, Jinzheng, Huo, Yuankai, Huang, Lingyun, Xia, Yong, Xiao, Jing, Lu, Le, Hu, Heping, Harrison, Adam
Using radiological scans to identify liver tumors is crucial for proper patient treatment. This is highly challenging, as top radiologists only achieve F1 scores of roughly 80% (hepatocellular carcinoma (HCC) vs. others) with only moderate inter-rate
Externí odkaz:
http://arxiv.org/abs/2012.06964
Autor:
Zhou, Xiao-Yun, Sun, Jiacheng, Ye, Nanyang, Lan, Xu, Luo, Qijun, Lai, Bo-Lin, Esperanca, Pedro, Yang, Guang-Zhong, Li, Zhenguo
Deep Convolutional Neural Networks (DCNNs) are hard and time-consuming to train. Normalization is one of the effective solutions. Among previous normalization methods, Batch Normalization (BN) performs well at medium and large batch sizes and is with
Externí odkaz:
http://arxiv.org/abs/2012.02782