Zobrazeno 1 - 10
of 191
pro vyhledávání: '"Zhao, Ziheng"'
Autor:
Li, Haolin, Zhou, Yuhang, Zhao, Ziheng, Du, Siyuan, Yao, Jiangchao, Xie, Weidi, Zhang, Ya, Wang, Yanfeng
The widespread adoption of large-scale pre-training techniques has significantly advanced the development of medical foundation models, enabling them to serve as versatile tools across a broad range of medical tasks. However, despite their strong gen
Externí odkaz:
http://arxiv.org/abs/2409.19540
Developing generalist foundation model has recently attracted tremendous attention among researchers in the field of AI for Medicine (AI4Medicine). A pivotal insight in developing these models is their reliance on dataset scaling, which emphasizes th
Externí odkaz:
http://arxiv.org/abs/2404.16754
In this study, we aim to build up a model that can Segment Anything in radiology scans, driven by Text prompts, termed as SAT. Our main contributions are three folds: (i) for dataset construction, we construct the first multi-modal knowledge tree on
Externí odkaz:
http://arxiv.org/abs/2312.17183
Autor:
Wu, Chaoyi, Lei, Jiayu, Zheng, Qiaoyu, Zhao, Weike, Lin, Weixiong, Zhang, Xiaoman, Zhou, Xiao, Zhao, Ziheng, Zhang, Ya, Wang, Yanfeng, Xie, Weidi
Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public. In this study, we aim to assess the performance of OpenAI's newest mod
Externí odkaz:
http://arxiv.org/abs/2310.09909
Autor:
Zhang, Xiaoman, Wu, Chaoyi, Zhao, Ziheng, Lin, Weixiong, Zhang, Ya, Wang, Yanfeng, Xie, Weidi
Medical Visual Question Answering (MedVQA) presents a significant opportunity to enhance diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret and answer questions based on medical images. In this study, we re
Externí odkaz:
http://arxiv.org/abs/2305.10415
Autor:
Lin, Weixiong, Zhao, Ziheng, Zhang, Xiaoman, Wu, Chaoyi, Zhang, Ya, Wang, Yanfeng, Xie, Weidi
Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M
Externí odkaz:
http://arxiv.org/abs/2303.07240
This paper considers the problem of undersampled MRI reconstruction. We propose a novel Transformer-based framework for directly processing signal in k-space, going beyond the limitation of regular grids as ConvNets do. We adopt an implicit represent
Externí odkaz:
http://arxiv.org/abs/2206.06947
Publikováno v:
In Journal of Environmental Management November 2024 370
Publikováno v:
In Process Safety and Environmental Protection October 2024 190 Part A:368-380
Publikováno v:
In Heliyon 15 September 2024 10(17)