A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases

作者：

Highlights：

• We developed an algorithm to identify trainee's false positive errors on DBT.

• The algorithm is personalized for each individual trainee.

• Our model can improve trainees’ training by focusing on false positive error cases.

摘要

•We developed an algorithm to identify trainee's false positive errors on DBT.•The algorithm is personalized for each individual trainee.•Our model can improve trainees’ training by focusing on false positive error cases.

论文关键词：Radiology education,False positive error prediction,Training plan optimization,Digital breast tomosynthesis,Image processing,Clustering

论文评审过程：Received 23 April 2016, Revised 13 June 2016, Accepted 3 August 2016, Available online 4 August 2016, Version of Record 10 August 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.08.023