Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation.

导出 LinkOut

翻译打印收藏纠错

作者： Craig K,Jones ^[1] ; Guoqing,Wang ^[2] ; Vivek,Yedavalli ^[3] ; Haris,Sair ^[4]

作者单位： Johns Hopkins University, Malone Center for Engineering in Healthcare, Baltimore, Maryland, United States. ^[1] Johns Hopkins University, Radiology AI Lab, Baltimore, Maryland, United States. ^[2] Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics, Baltimore, Maryland, United States. ^[3] Johns Hopkins Hospital, Russell H. Morgan Department of Radiology and Radiological Sciences, Baltimore, Maryland, United States. ^[4]

关键词 U-Net segmentation aleatoric uncertainty epistemic uncertainty neural networks

DOI 10.1117/1.JMI.9.3.034002

PMID 35692283

发布时间 2023-06-10

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

Journal of medical imaging (Bellingham, Wash.)

2022年9卷3期

034002页

相似文献

中文期刊
外文期刊
学位论文
会议论文

检索历史清除

Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation.

Journal of medical imaging (Bellingham, Wash.)

相似文献

Journal of medical imaging (Bellingham, Wash.)

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation.

Journal of medical imaging (Bellingham, Wash.)

相似文献

Journal of medical imaging (Bellingham, Wash.)

相关期刊

检索历史清除