_:n6004393b30b04ac5946ecd0c32b7cc5bxb36
<http://schema.org/description>
"We propose an unsupervised method using self-clustering convolutional\nadversarial autoencoders to classify prostate tissue as tumor or non-tumor\nwithout any labeled training data. The clustering method is integrated into the\ntraining of the autoencoder and requires only little post-processing. Our\nnetwork trains on hematoxylin and eosin (H&E) input patches and we tested two\ndifferent reconstruction targets, H&E and immunohistochemistry (IHC). We show\nthat antibody-driven feature learning using IHC helps the network to learn\nrelevant features for the clustering task. Our network achieves a F1 score of\n0.62 using only a small set of validation labels to assign classes to clusters."
<https://deepai.org/profile/wouter-bulten>