###### Abstract  
Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN. prostate cancer detection; magnetic resonance imaging; convolutional neural networks; conditional random fields; recurrent neural networks +
Footnote †: journal: Medical  
techniques--i.e., Digital Rectal Exam, Prostate-Specific Antigen (PSA) [3]--may be subjective and error-prone [4]. Furthermore, intra-tumor heterogeneity is observed in PCa, contributing to disease progression [5]. Currently, high-resolution multiparametric Magnetic Resonance Imaging (mpMRI) for Computer-Aided Diagnosis (CAD) is gaining clinical and scientific interest [6] by enabling quantitative measurements for intra- and inter-tumoral heterogeneity based on radiomics studies [7]. Additional and often complementary information can be acquired by means of different MRI sequences: anatomical information can be obtained using T2-weighted (T2w), T1-weighted (T1w) and Proton Density (PDw) protocols [4; 8; 9]. Further information is conveyed by functional imaging [10], allowing for better depiction of multiple aspects of the tumor structure: its micro-environment by estimating the water molecule movement using Diffusion-Weighted Imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps [11], as well as the vascular structure of the tumor with Dynamic Contrast-Enhanced (DCE) MRI [12]. Unfortunately, multi-focal tumors in the prostate occur commonly, posing additional challenges for accurate prognoses on MRI [13]; thus, devising and exploiting advanced Machine Learning methods [14; 15] for prostate cancer detection and differentiation is clinically relevant [16]. Therefore, the tasks of PCa classification can benefit from the combination of several modalities, each conveying clinically useful information [8; 17]. Clinical consensus for PCa diagnosis typically considers mpMRI by combining T2w with at least two functional imaging protocols [18]. In this work, T2w, PDw and ADC MRI sequences were chosen as inputs for the models. T2w conveys relevant information about the prostate zonal anatomy [15] as well as tumor location and extent [19]: PCa has low signal intensity, which can be suitably detected from the healthy hyper-intense peripheral zone tissue (harboring approximately 70% of PCa cases [20]), but it is more difficult to differentiate in the central and transitional zones due to their normal low signal intensity [4; 8].