_:n6004393b30b04ac5946ecd0c32b7cc5bxb42
<http://schema.org/description>
"Large amounts of unlabelled data are commonplace for many applications in\ncomputational pathology, whereas labelled data is often expensive, both in time\nand cost, to acquire. We investigate the performance of unsupervised and\nsupervised deep learning methods when few labelled data are available. Three\nmethods are compared: clustering autoencoder latent vectors (unsupervised), a\nsingle layer classifier combined with a pre-trained autoencoder\n(semi-supervised), and a supervised CNN. We apply these methods on hematoxylin\nand eosin (H&E) stained prostatectomy images to classify tumour versus\nnon-tumour tissue. Results show that semi-/unsupervised methods have an\nadvantage over supervised learning when few labels are available. Additionally,\nwe show that incorporating immunohistochemistry (IHC) stained data provides an\nincrease in performance over only using H&E."
<https://deepai.org/profile/wouter-bulten>