A Decomposable Model for the Detection of Prostate Cancer in Multi-parametric MRI
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 research was funded by the Intramural Research Program of the National Institutes of Health, Clinical Center. Data used in this research were obtained from The Cancer Imaging Archive (TCIA) sponsored by the SPIE, NCI/NIH, AAPM, and Radboud University.
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