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Traditional deep learning (DL) approaches based on supervised learningparadigms require large amounts of annotated data that are rarely available inthe medical domain. Unsupervised Out-of-distribution (OOD) detection is analternative that requires less annotated data. Further, OOD applicationsexploit the class skewness commonly present in medical data. Magnetic resonanceimaging (MRI) has proven to be useful for prostate cancer (PCa) diagnosis andmanagement, but current DL approaches rely on T2w axial MRI, which suffers fromlow out-of-plane resolution. We propose a multi-stream approach to accommodatedifferent T2w directions to improve the performance of PCa lesion detection inan OOD approach. We evaluate our approach on a publicly available data-set,obtaining better detection results in terms of AUC when compared to a singledirection approach (73.1 vs 82.3). Our results show the potential of OODapproaches for PCa lesion detection based on MRI.

Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection