## 5 Discussion and Conclusions  
In this work, the potential of integrating the CRF-RNN mechanism into a CNN architecture is presented, not only for MIA applications but also for the Image Classification field in general-purpose Computer Vision. This joint approach is built upon previous work that has shown that the combination of CRFs and CNNs in a hybrid end-to-end model can achieve promising performance across several benchmark datasets in image segmentation tasks [31; 47; 49; 51; 52; 70]. The proposed CRF-XmasNet architecture leads to an interesting improvement over its baseline architecture (XmasNet [33]), and its best performance is comparable with the one obtained with deeper neural architectures, namely: AlexNet [34], VGG16 [44], and ResNet [45]. Additionally, our work showed that the use of CRFs as a postprocessing method is not suitable for the classification problem taken into account. This result corroborates the analysis reported in [31]. CRF-RNN can achieve competitive performance by also reducing the training time when compared against the baseline architecture. Despite these advantages, the integration of CRFs produces results characterized by a higher variability when compared against the other considered architectures. This phenomenon was observed also when the CRF-RNN component was integrated into AlexNet and VGG16. In this case, the two resulting architectures (i.e., CRF-AlexNet and CRF-VGG16) were characterized by high-variability of performance on both training and test sets. The amount of homogeneous and well-prepared datasets represents an important challenge in biomedical imaging [24]. As a matter of fact, Deep Learning research has been recently focusing on issues related to medical imaging datasets with limited sample size, achieving promising performance by means of weakly-/semi-supervised schemes [49; 71] as well as Generative Adversarial Network (GAN)-based data augmentation [72; 73]. Moreover, methods tailored to each particular clinical application should be devised, such as for improving the model generalization abilities even in the case of small datasets collected from multiple institutions [15]. Given the common ground that the Machine Learning and Image Classification fields share, more promising and robust performance may be achieved by the further integration of CRFs into CNNs. For instance, CNN architecture tuning [74] might reduce the variability encountered in the experiments involving CRF-XmasNet and the other CNNs embedding CRFs. This contribution can open additional research directions aimed at investigating the performance variability of the CRF-RNNs when integrated into CNNs as an end-to-end approach, thus allowing for their use in a clinical environment. In conclusion, the proposed end-to-end PCa detection approach might be used as a CAD, before the conventional mpMRI interpretation by experienced radiologists, aiming at increasing sensitivity and reducing operator dependence upon multiple readers [6]. To improve CS PCa classification performance, the combination with metabolic imaging might provide complementary clinical insights into tumor responses to oncological therapies [75]. Novel nuclear medicine tracers for Positron Emission Tomography (PET) [76] and hyperpolarized carbon-13 (\({}^{13}\)C) and sodium (\({}^{23}\)Na) MRI [77] can considerably improve the specificity for evaluating PCa with respect to conventional imaging, by understanding the pyruvate conversion to lactate for estimating the cancer grade [78]. From a computational perspective, novel solutions must be devised to combine multi-modal imaging data [79]. In the case of DNNs, a topology explicitly designed for information exchange--between sub-networks processing the data from a single modality--through cross-connections, such as in the case of cross-modal CNNs (X-CNNs) [80], might be suitable for combining multi-modal imaging data. Conceptualization, M.C. and L.R. ; Investigation, P.L., M.C. and L.R. ; Methodology, P.L., M.C. and L.R. ; Software, P.L. ; Supervision, M.C. and L.R.