Adversarial Networks for Prostate Cancer Detection

TL;DR

This paper introduces an adversarial training approach for prostate cancer segmentation in MRI images, improving detection sensitivity and accuracy, especially with limited data.

Contribution

It proposes a novel adversarial training scheme for prostate cancer segmentation that addresses data scarcity and annotation ambiguity issues.

Findings

Superior detection sensitivity over standard methods

Higher dice-score for aggressive prostate cancer

Effective in small dataset scenarios

Abstract

The large number of trainable parameters of deep neural networks renders them inherently data hungry. This characteristic heavily challenges the medical imaging community and to make things even worse, many imaging modalities are ambiguous in nature leading to rater-dependant annotations that current loss formulations fail to capture. We propose employing adversarial training for segmentation networks in order to alleviate aforementioned problems. We learn to segment aggressive prostate cancer utilizing challenging MRI images of 152 patients and show that the proposed scheme is superior over the de facto standard in terms of the detection sensitivity and the dice-score for aggressive prostate cancer. The achieved relative gains are shown to be particularly pronounced in the small dataset limit.