  Magnetic Resonance Imaging (MRI) is widely used for screening and staging
prostate cancer. However, many prostate cancers have subtle features which are
not easily identifiable on MRI, resulting in missed diagnoses and alarming
variability in radiologist interpretation. Machine learning models have been
developed in an effort to improve cancer identification, but current models
localize cancer using MRI-derived features, while failing to consider the
disease pathology characteristics observed on resected tissue. In this paper,
we propose CorrSigNet, an automated two-step model that localizes prostate
cancer on MRI by capturing the pathology features of cancer. First, the model
learns MRI signatures of cancer that are correlated with corresponding
histopathology features using Common Representation Learning. Second, the model
uses the learned correlated MRI features to train a Convolutional Neural
Network to localize prostate cancer. The histopathology images are used only in
the first step to learn the correlated features. Once learned, these correlated
features can be extracted from MRI of new patients (without histopathology or
surgery) to localize cancer. We trained and validated our framework on a unique
dataset of 75 patients with 806 slices who underwent MRI followed by
prostatectomy surgery. We tested our method on an independent test set of 20
prostatectomy patients (139 slices, 24 cancerous lesions, 1.12M pixels) and
achieved a per-pixel sensitivity of 0.81, specificity of 0.71, AUC of 0.86 and
a per-lesion AUC of $0.96 \pm 0.07$, outperforming the current state-of-the-art
accuracy in predicting prostate cancer using MRI.
