Zobrazeno 1 - 10
of 358
pro vyhledávání: '"ZHAN Jianfeng"'
Publikováno v:
Xiehe Yixue Zazhi, Vol 14, Iss 6, Pp 1135-1141 (2023)
The standardization of medical artificial intelligence (AI) is currently in its infancy and falls short of meeting the needs for the development, deployment, control, assessment, and guidance of medical AI products. This not only makes it difficult t
Externí odkaz:
https://doaj.org/article/5b9eff56e5f04bbba54832b5407c7439
The SPEC CPU2017 benchmark suite is an industry standard for accessing CPU performance. It adheres strictly to some workload and system configurations - arbitrary specificity - while leaving other system configurations undefined - arbitrary ambiguity
Externí odkaz:
http://arxiv.org/abs/2411.08494
Publikováno v:
Xiehe Yixue Zazhi, Vol 12, Iss 5, Pp 614-620 (2021)
Big medical data and medical artificial intelligence (AI) not only have the great potential for improving the utilization of medical resources and the quality of medical service, but also pose challenges to privacy protection and technical risks. Sta
Externí odkaz:
https://doaj.org/article/516e782565db480a883a0140e54f6eb8
Autor:
Kang, Guoxin, Gao, Wanling, Wang, Lei, Luo, Chunjie, Ye, Hainan, He, Qian, Dai, Shaopeng, Zhan, Jianfeng
By utilizing statistical methods to analyze bibliographic data, bibliometrics faces inherent limitations in identifying the most significant science and technology achievements and researchers. To overcome this challenge, we present an evaluatology-b
Externí odkaz:
http://arxiv.org/abs/2408.12158
Autor:
Gao, Wanling, Huang, Yunyou, Cui, Dandan, Yu, Zhuoming, Liu, Wenjing, Liang, Xiaoshuang, Zhao, Jiahui, Xie, Jiyue, Li, Hao, Ma, Li, Ye, Ning, Kang, Yumiao, Luo, Dingfeng, Pan, Peng, Huang, Wei, Liu, Zhongmou, Hu, Jizhong, Zhao, Gangyuan, Jiang, Chongrong, Huang, Fan, Wei, Tianyi, Tang, Suqin, Xia, Bingjie, Zhang, Zhifei, Zhan, Jianfeng
A profound gap persists between artificial intelligence (AI) and clinical practice in medicine, primarily due to the lack of rigorous and cost-effective evaluation methodologies. State-of-the-art and state-of-the-practice AI model evaluations are lim
Externí odkaz:
http://arxiv.org/abs/2407.08554
Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement. This dependency presents a significant barrier to rapid innovation. Recognizing t
Externí odkaz:
http://arxiv.org/abs/2406.15132
Autor:
Gao, Wanling, Liu, Yuan, Yu, Zhuoming, Cui, Dandan, Liu, Wenjing, Liang, Xiaoshuang, Zhao, Jiahui, Xie, Jiyue, Li, Hao, Ma, Li, Ye, Ning, Kang, Yumiao, Luo, Dingfeng, Pan, Peng, Huang, Wei, Liu, Zhongmou, Hu, Jizhong, Huang, Fan, Zhao, Gangyuan, Jiang, Chongrong, Wei, Tianyi, Zhang, Zhifei, Huang, Yunyou, Zhan, Jianfeng
Artificial Intelligence (AI) plays a crucial role in medical field and has the potential to revolutionize healthcare practices. However, the success of AI models and their impacts hinge on the synergy between AI and medical specialists, with clinicia
Externí odkaz:
http://arxiv.org/abs/2406.07362
The rapid development of domain-specific frameworks has presented us with a significant challenge: The current approach of implementing solutions on a case-by-case basis incurs a theoretical complexity of O(M*N), thereby increasing the cost of portin
Externí odkaz:
http://arxiv.org/abs/2405.12491
Autor:
Zhan, Jianfeng, Wang, Lei, Gao, Wanling, Li, Hongxiao, Wang, Chenxi, Huang, Yunyou, Li, Yatao, Yang, Zhengxin, Kang, Guoxin, Luo, Chunjie, Ye, Hainan, Dai, Shaopeng, Zhang, Zhifei
Evaluation is a crucial aspect of human existence and plays a vital role in various fields. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on universal concepts, terminologies, theories, and methodologies. This l
Externí odkaz:
http://arxiv.org/abs/2404.00021
The burgeoning field of Artificial Intelligence Generated Content (AIGC) is witnessing rapid advancements, particularly in video generation. This paper introduces AIGCBench, a pioneering comprehensive and scalable benchmark designed to evaluate a var
Externí odkaz:
http://arxiv.org/abs/2401.01651