Medical volume segmentation by overfitting sparsely annotated data.

Tristan Payer,Faraz Nizamani,Meinrad Beer,Michael Götz,Timo Ropinski

Journal of medical imaging (Bellingham, Wash.)（2023）

引用 0|浏览12

暂无评分

摘要

We evaluate a method that supports experts during the labeling of 3D medical volumes. Our approach makes it possible to drastically reduce the number of slices that need to be manually labeled. We present a recommendation in which selector predictor combination to use for different tasks and goals.

查看译文

关键词

image segmentation, data labeling, active learning, neural networks, artificial intelligence

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要