Zobrazeno 1 - 10
of 1 124
pro vyhledávání: '"Wang, Xiaosong"'
Autor:
Chen, Kaitao, Liu, Mianxin, Yan, Fang, Ma, Lei, Shi, Xiaoming, Wang, Lilong, Wang, Xiaosong, Zhu, Lifeng, Wang, Zhe, Zhou, Mu, Zhang, Shaoting
The advent of vision-language models fosters the interactive conversations between AI-enabled models and humans. Yet applying these models into clinics must deal with daunting challenges around large-scale training data, financial, and computational
Externí odkaz:
http://arxiv.org/abs/2407.17734
Survival analysis stands as a pivotal process in cancer treatment research, crucial for predicting patient survival rates accurately. Recent advancements in data collection techniques have paved the way for enhancing survival predictions by integrati
Externí odkaz:
http://arxiv.org/abs/2407.17726
Autor:
Qu, Linhao, Yang, Dingkang, Huang, Dan, Guo, Qinhao, Luo, Rongkui, Zhang, Shaoting, Wang, Xiaosong
Current multi-instance learning algorithms for pathology image analysis often require a substantial number of Whole Slide Images for effective training but exhibit suboptimal performance in scenarios with limited learning data. In clinical settings,
Externí odkaz:
http://arxiv.org/abs/2407.10814
Autor:
Wang, Xiaosong, Zhang, Xiaofan, Wang, Guotai, He, Junjun, Li, Zhongyu, Zhu, Wentao, Guo, Yi, Dou, Qi, Li, Xiaoxiao, Wang, Dequan, Hong, Liang, Lao, Qicheng, Ruan, Tong, Zhou, Yukun, Li, Yixue, Zhao, Jie, Li, Kang, Sun, Xin, Zhu, Lifeng, Zhang, Shaoting
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas. However, domain-specific applications of s
Externí odkaz:
http://arxiv.org/abs/2402.18028
The emergence of multi-modal deep learning models has made significant impacts on clinical applications in the last decade. However, the majority of models are limited to single-tasking, without considering disease diagnosis is indeed a multi-task pr
Externí odkaz:
http://arxiv.org/abs/2311.01092
Autor:
Du, Shiyi, Wang, Xiaosong, Lu, Yongyi, Zhou, Yuyin, Zhang, Shaoting, Yuille, Alan, Li, Kang, Zhou, Zongwei
Image synthesis approaches, e.g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks. It is primarily beneficial to overcome the shortage of publicly accessible data and associated qualit
Externí odkaz:
http://arxiv.org/abs/2310.02906
The recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with strong generalizability. Yet pathological image datasets often lack biome
Externí odkaz:
http://arxiv.org/abs/2307.14901
Deep learning models often require large amounts of data for training, leading to increased costs. It is particularly challenging in medical imaging, i.e., gathering distributed data for centralized training, and meanwhile, obtaining quality labels r
Externí odkaz:
http://arxiv.org/abs/2306.14113
Autor:
Wang, Dequan, Wang, Xiaosong, Wang, Lilong, Li, Mengzhang, Da, Qian, Liu, Xiaoqiang, Gao, Xiangyu, Shen, Jun, He, Junjun, Shen, Tian, Duan, Qi, Zhao, Jie, Li, Kang, Qiao, Yu, Zhang, Shaoting
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using onl
Externí odkaz:
http://arxiv.org/abs/2306.09579
Autor:
Wang, Jialu1 (AUTHOR) hsdzqy@stu.hrbnu.edu.cn, Wang, Xiaosong1 (AUTHOR), Liu, Lihui1 (AUTHOR), Wang, Xiang1 (AUTHOR), Wang, Jiarui1 (AUTHOR), Zheng, Yue1 (AUTHOR), Wang, Li1 (AUTHOR), Pan, Xuming1 (AUTHOR) panxuming@hrbnu.edu.cn
Publikováno v:
Animals (2076-2615). Aug2024, Vol. 14 Issue 15, p2194. 19p.