  We propose an unsupervised method using self-clustering convolutional
adversarial autoencoders to classify prostate tissue as tumor or non-tumor
without any labeled training data. The clustering method is integrated into the
training of the autoencoder and requires only little post-processing. Our
network trains on hematoxylin and eosin (H&E) input patches and we tested two
different reconstruction targets, H&E and immunohistochemistry (IHC). We show
that antibody-driven feature learning using IHC helps the network to learn
relevant features for the clustering task. Our network achieves a F1 score of
0.62 using only a small set of validation labels to assign classes to clusters.
