Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification

TL;DR

This paper introduces a novel image translation and uncertainty-aware training framework for prostate cancer classification using MRI data, addressing data scarcity and domain differences in clinical settings.

Contribution

It presents a new pipeline for translating unpaired MRI data between different magnetic field strengths and an evidential deep learning method with a specialized loss for uncertainty estimation.

Findings

Improved AUC by over 20% compared to previous methods

Effective data augmentation through MRI translation

Enhanced model reliability via uncertainty estimation

Abstract

Prostate Cancer (PCa) is a prevalent disease among men, and multi-parametric MRIs offer a non-invasive method for its detection. While MRI-based deep learning solutions have shown promise in supporting PCa diagnosis, acquiring sufficient training data, particularly in local clinics remains challenging. One potential solution is to take advantage of publicly available datasets to pre-train deep models and fine-tune them on the local data, but multi-source MRIs can pose challenges due to cross-domain distribution differences. These limitations hinder the adoption of explainable and reliable deep-learning solutions in local clinics for PCa diagnosis. In this work, we present a novel approach for unpaired image-to-image translation of prostate multi-parametric MRIs and an uncertainty-aware training approach for classifying clinically significant PCa, to be applied in data-constrained settings such as local and small clinics. Our approach involves a novel pipeline for translating unpaired 3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available training data. Additionally, we introduce an evidential deep learning approach to estimate model uncertainty and employ dataset filtering techniques during training. Furthermore, we propose a simple, yet efficient Evidential Focal Loss, combining focal loss with evidential uncertainty, to train our model effectively. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work. Our code is available at https://github.com/med-i-lab/DT_UE_PCa