Geometric multimodal learning to support prostate cancer diagnosis on limited and multicentric bi-parametric MRI data

The classification of clinically significant prostate cancer (csPCa) lesions remains one of the most important challenges in prostate cancer diagnosis. For this, multimodal convolutional neural networks (CNNs) have achieved outstanding results. Nevertheless, the data used in these studies may only partially represent the total burden of csPCa cases. Hence, it is necessary to design reliable models that perform well in limited data scenarios and involving information from different centers (multicentric). A deep Riemannian geometric learning architecture was introduced to capture the intermediate relationships between bi-parametric MRI (bp-MRI) deep representations coded from a 3D multimodal convolutional backbone and considering their geometry. For this, several multimodal bp-MRI fusion strategies were explored to assess their ability to classify csPCa lesions in scenarios where the percentage of available training data was progressively reduced and multicentric data were involved. The proposed method outperformed baseline CNN techniques with an AUC-ROC of 0.96. More remarkably, the method remained stable even only using 10% of the available training data. Additionally, considering multicentric information, this approach also demonstrates generalization ability by losing only 5.4% of the AUC testing data from different acquisition centers, compared to the 10.4% loss of the baseline method. A new deep learning-based method that improves generalization under scenarios with limited data translates to better support for clinicians in accurately classifying csPCa lesions on unseen data.