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Found 4 papers, 4 papers with code
Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides
Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists.
Adversarial learning of cancer tissue representations
We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations.
Learning a low dimensional manifold of real cancer tissue with PathologyGAN
We present a deep generative model that learns to simulate high-fidelity cancer tissue images while mapping the real images onto an interpretable low dimensional latent space.
PathologyGAN: Learning deep representations of cancer tissue
We show that our model generates high quality images, with a FID of 16. 65 (breast cancer) and 32. 05 (colorectal cancer).
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