Abstract #1830

Improving prostate cancer detection in mp-MRI via CNN using the joint loss

Ruiming Cao^1,2, Xinran Zhong¹, Amirhossein Mohammadian Bajgiran¹, Sohrab Afshari Mirak¹, Sepideh Shakeri¹, Fabien Scalzo², Steven Raman¹, and Kyunghyun Sung¹

¹Radiology, University of California, Los Angeles, Los Angeles, CA, United States, ²Computer Science, University of California, Los Angeles, Los Angeles, CA, United States

We proposed an improved CNN using joint loss to fully utilize multi-parametric imaging for the automated prostate cancer detection in mp-MRI. 397 pre-operative mp-MRI exams were collected in our medical center, and lesion ROIs were retrospectively annotated with whole-mount histopathology confirmations. The improved CNN achieved 75.1% detection sensitivity at 1 false positive per patient and had an AUC 0.901 in the ROC analysis.

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