Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders

Wouter Bulten, Geert Litjens

Apr 11, 2018 MIDL 2018 Abstract Submission readers: everyone
  • Abstract: 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.
  • Author affiliation: Radboud University Medical Center
  • Keywords: deep learning, autoencoders, unsupervised learning, prostate cancer, pathology, immunohistochemistry
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