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Super resolution of histopathological frozen sections via deep learning preserving tissue structure
Our deep-learning architecture focuses on learning the error between interpolated images and real images, thereby it generates high-resolution images while preserving critical image details, reducing the risk of diagnostic misinterpretation.
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set
After this preliminary training, and after transforming the last layer of the network with new ones, we have designed an automatic classifier for the correct cell type (healthy/primary cancer/metastatic cancer) with 90-99% accuracy, although small training sets of down to several images have been used.
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