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

arXiv: https://arxiv.org/abs/2308.06481 · HTML: https://mlsanity.com/paper/2308.06481/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 12 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders](https://mlsanity.com/paper/1806.04972/) (2018)
- [3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI](https://mlsanity.com/paper/2212.14267/) (2022)

## Abstract (arXiv)

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.