期刊文献+

基于人工智能的医学影像辅助诊断类软件监管与评测方法研究 被引量:2

下载PDF
导出
摘要 本文基于人工智能医学影像辅助诊断类软件技术现状,梳理现有监管政策及评测体系,针对尚未有与该类软件技术特性相匹配的评价方式等问题,提出了一套回顾性数据集的性能评测方法。该方法能在一定程度上规范该类产品的技术标准,为监管机构提供有力的监管依据,推动其持续健康发展。
出处 《电子技术与软件工程》 2022年第4期77-82,共6页 ELECTRONIC TECHNOLOGY & SOFTWARE ENGINEERING
  • 相关文献

参考文献6

二级参考文献107

  • 1LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
  • 2HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18(7): 1527-1554.
  • 3LEE H, GROSSE R, RANGANATH R, et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations [C]// ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning. New York: ACM, 2009: 609-616.
  • 4HUANG G B, LEE H, ERIK G. Learning hierarchical representations for face verification with convolutional deep belief networks [C]// CVPR '12: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2012: 2518-2525.
  • 5KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [C]// Proceedings of Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2012: 1106-1114.
  • 6GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2014: 580-587.
  • 7LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation [C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2015: 3431-3440.
  • 8SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [EB/OL]. [2015-11-04]. http://www.robots.ox.ac.uk:5000/~vgg/publications/2015/Simonyan15/simonyan15.pdf.
  • 9SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions [C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2015: 1-8.
  • 10HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [EB/OL]. [2016-01-04]. https://www.researchgate.net/publication/286512696_Deep_Residual_Learning_for_Image_Recognition.

共引文献712

同被引文献42

引证文献2

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部