Zobrazeno 1 - 10
of 8 243
pro vyhledávání: '"Ye, JIN"'
Autor:
Hu, Ming, Yuan, Kun, Shen, Yaling, Tang, Feilong, Xu, Xiaohao, Zhou, Lin, Li, Wei, Chen, Ying, Xu, Zhongxing, Peng, Zelin, Yan, Siyuan, Srivastav, Vinkle, Song, Diping, Li, Tianbin, Shi, Danli, Ye, Jin, Padoy, Nicolas, Navab, Nassir, He, Junjun, Ge, Zongyuan
Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challenging due to this complexity and the limited availability of annotated d
Externí odkaz:
http://arxiv.org/abs/2411.15421
Autor:
Ye, Jin, Chen, Ying, Li, Yanjun, Wang, Haoyu, Deng, Zhongying, Huang, Ziyan, Su, Yanzhou, Ma, Chenglong, Ji, Yuanfeng, He, Junjun
Computed Tomography (CT) is one of the most popular modalities for medical imaging. By far, CT images have contributed to the largest publicly available datasets for volumetric medical segmentation tasks, covering full-body anatomical structures. Lar
Externí odkaz:
http://arxiv.org/abs/2411.14525
Autor:
Li, Tianbin, Su, Yanzhou, Li, Wei, Fu, Bin, Chen, Zhe, Huang, Ziyan, Wang, Guoan, Ma, Chenglong, Chen, Ying, Hu, Ming, Li, Yanjun, Chen, Pengcheng, Hu, Xiaowei, Deng, Zhongying, Ji, Yuanfeng, Ye, Jin, Qiao, Yu, He, Junjun
Despite significant advancements in general artificial intelligence, such as GPT-4, their effectiveness in the medical domain (general medical AI, GMAI) remains constrained due to the absence of specialized medical knowledge. To address this challeng
Externí odkaz:
http://arxiv.org/abs/2411.14522
Autor:
Cheng, Junlong, Fu, Bin, Ye, Jin, Wang, Guoan, Li, Tianbin, Wang, Haoyu, Li, Ruoyu, Yao, He, Chen, Junren, Li, Jingwen, Su, Yanzhou, Zhu, Min, He, Junjun
Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this
Externí odkaz:
http://arxiv.org/abs/2411.12814
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Autor:
Bassi, Pedro R. A. S., Li, Wenxuan, Tang, Yucheng, Isensee, Fabian, Wang, Zifu, Chen, Jieneng, Chou, Yu-Cheng, Kirchhoff, Yannick, Rokuss, Maximilian, Huang, Ziyan, Ye, Jin, He, Junjun, Wald, Tassilo, Ulrich, Constantin, Baumgartner, Michael, Roy, Saikat, Maier-Hein, Klaus H., Jaeger, Paul, Ye, Yiwen, Xie, Yutong, Zhang, Jianpeng, Chen, Ziyang, Xia, Yong, Xing, Zhaohu, Zhu, Lei, Sadegheih, Yousef, Bozorgpour, Afshin, Kumari, Pratibha, Azad, Reza, Merhof, Dorit, Shi, Pengcheng, Ma, Ting, Du, Yuxin, Bai, Fan, Huang, Tiejun, Zhao, Bo, Wang, Haonan, Li, Xiaomeng, Gu, Hanxue, Dong, Haoyu, Yang, Jichen, Mazurowski, Maciej A., Gupta, Saumya, Wu, Linshan, Zhuang, Jiaxin, Chen, Hao, Roth, Holger, Xu, Daguang, Blaschko, Matthew B., Decherchi, Sergio, Cavalli, Andrea, Yuille, Alan L., Zhou, Zongwei
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a con
Externí odkaz:
http://arxiv.org/abs/2411.03670
In today's machine learning world for tabular data, XGBoost and fully connected neural network (FCNN) are two most popular methods due to their good model performance and convenience to use. However, they are highly complicated, hard to interpret, an
Externí odkaz:
http://arxiv.org/abs/2410.19154
Autor:
Guo, Zhangyuan, Ge, Min, Zhou, You-Qi, Bi, Jiachang, Zhang, Qinghua, Zhang, Jiahui, Ye, Jin-Tao, Zhai, Rongjing, Ge, Fangfang, Huang, Yuan, Zhang, Ruyi, Yao, Xiong, Huang, Liang-Feng, Cao, Yanwei
Publikováno v:
Materials Horizons 2024
Superconductors, an essential class of functional materials, hold a vital position in both fundamental science and practical applications. However, most superconductors, including MgB$_2$, Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, and FeSe, are highly sens
Externí odkaz:
http://arxiv.org/abs/2410.17588
Autor:
Chen, Ying, Wang, Guoan, Ji, Yuanfeng, Li, Yanjun, Ye, Jin, Li, Tianbin, Zhang, Bin, Pei, Nana, Yu, Rongshan, Qiao, Yu, He, Junjun
Despite the progress made by multimodal large language models (MLLMs) in computational pathology, they remain limited by a predominant focus on patch-level analysis, missing essential contextual information at the whole-slide level. The lack of large
Externí odkaz:
http://arxiv.org/abs/2410.11761
Autor:
Das, Adrito, Khan, Danyal Z., Psychogyios, Dimitrios, Zhang, Yitong, Hanrahan, John G., Vasconcelos, Francisco, Pang, You, Chen, Zhen, Wu, Jinlin, Zou, Xiaoyang, Zheng, Guoyan, Qayyum, Abdul, Mazher, Moona, Razzak, Imran, Li, Tianbin, Ye, Jin, He, Junjun, Płotka, Szymon, Kaleta, Joanna, Yamlahi, Amine, Jund, Antoine, Godau, Patrick, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Rivoir, Dominik, Pérez, Alejandra, Rodriguez, Santiago, Arbeláez, Pablo, Stoyanov, Danail, Marcus, Hani J., Bano, Sophia
The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed; and which surgical
Externí odkaz:
http://arxiv.org/abs/2409.01184
Autor:
Wang, Chong, Li, Mengyao, He, Junjun, Wang, Zhongruo, Darzi, Erfan, Chen, Zan, Ye, Jin, Li, Tianbin, Su, Yanzhou, Ke, Jing, Qu, Kaili, Li, Shuxin, Yu, Yi, Liò, Pietro, Wang, Tianyun, Wang, Yu Guang, Shen, Yiqing
Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking
Externí odkaz:
http://arxiv.org/abs/2409.00133