A Decomposable Model for the Detection of Prostate Cancer in Multi-parametric MRI

Nathan Lay, Yohannes Tsehay, Yohan Sumathipala, Ruida Cheng, Sonia Gaur, Clayton Smith, Adrian Barbu, Le Lu, Baris Turkbey, Peter L. Choyke, Peter A. Pinto, Ronald M. Summers

Abstract: 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.