Out-of-distribution multi-view auto-encoders for prostate cancer lesion
  detection
Alvaro Fernandez-Quilez Linas Vidziunas Ørjan Kløvfjell Thoresen Ketil Oppedal Svein Reidar Kjosavik Trygve Eftestøl
Traditional deep learning (DL) approaches based on supervised learning
paradigms require large amounts of annotated data that are rarely available in
the medical domain. Unsupervised Out-of-distribution (OOD) detection is an
alternative that requires less annotated data. Further, OOD applications
exploit the class skewness commonly present in medical data. Magnetic resonance
imaging (MRI) has proven to be useful for prostate cancer (PCa) diagnosis and
management, but current DL approaches rely on T2w axial MRI, which suffers from
low out-of-plane resolution. We propose a multi-stream approach to accommodate
different T2w directions to improve the performance of PCa lesion detection in
an OOD approach. We evaluate our approach on a publicly available data-set,
obtaining better detection results in terms of AUC when compared to a single
direction approach (73.1 vs 82.3). Our results show the potential of OOD
approaches for PCa lesion detection based on MRI.