Title: Adversarial Networks for the Detection of Aggressive Prostate Cancer,
Abstract: Semantic segmentation constitutes an integral part of medical image analyses
for which breakthroughs in the field of deep learning were of high relevance.
The large number of trainable parameters of deep neural networks however
renders them inherently data hungry, a characteristic that heavily challenges
the medical imaging community. Though interestingly, with the de facto standard
training of fully convolutional networks (FCNs) for semantic segmentation being
agnostic towards the `structure' of the predicted label maps, valuable
complementary information about the global quality of the segmentation lies
idle. In order to tap into this potential, we propose utilizing an adversarial
network which discriminates between expert and generated annotations in order
to train FCNs for semantic segmentation. Because the adversary constitutes a
learned parametrization of what makes a good segmentation at a global level, we
hypothesize that the method holds particular advantages for segmentation tasks
on complex structured, small datasets. This holds true in our experiments: We
learn to segment aggressive prostate cancer utilizing 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.