  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.
