A decomposable model for the detection of prostate cancer in multi-parametric MRI

Document Type

Conference Proceeding

Publication Date



Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


11071 LNCS




© Springer Nature Switzerland AG 2018. Institutions that specialize in prostate MRI acquire different MR sequences owing to variability in scanning procedure and scanner hardware. We propose a novel prostate cancer detector that can operate in the absence of MR imaging sequences. Our novel prostate cancer detector first trains a forest of random ferns on all MR sequences and then decomposes these random ferns into a sum of MR sequence-specific random ferns enabling predictions to be made in the absence of one or more of these MR sequences. To accomplish this, we first show that a sum of random ferns can be exactly represented by another random fern and then we propose a method to approximately decompose an arbitrary random fern into a sum of random ferns. We show that our decomposed detector can maintain good performance when some MR sequences are omitted.

This document is currently not available here.