Development of a measure for evaluating lesion‐wise performance of CAD algorithms in the context of mpMRI detection of prostate cancer
Abstract
Computer‐aided detection/diagnosis (CAD) of prostate cancer (PCa) on multiparametric MRI (mpMRI) is an active area of research. In the literature, the performance of predictive models trained to detect PCa on mpMRI has typically been reported in terms of voxel‐wise measures such as sensitivity and specificity and/or area under the receiver operating curve (AUC). However, it is unclear whether models that score higher by these measures are actually superior. Here, we propose a novel method for lesion identification as well as novel measures that assess the quality of the detected lesions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 16, 2018
- Source ID
- 10.1002/mp.12861
Entities
People
- Benjamin Spilseth
- Ethan Leng
- Gregory J. Metzger
- Jin Jin
- Joseph S Koopmeiners
- Lin Zhang
Organizations
- National Institutes of Health
- United States Department of Defense
- University of Minnesota