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- W2891483162 abstract "This paper proposes a method for improving the results of deep convolutional neural network classification using synthetic image samples. Generative adversarial networks are used to generate synthetic images from a dataset of phase-contrast, human embryonic stem cell (hESC) microscopy images. hESCnet, a deep convolutional neural network is trained, and the results are shown on various combinations of synthetic and real images in order to improve the classification results with minimal data." @default.
- W2891483162 created "2018-09-27" @default.
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- W2891483162 date "2018-10-01" @default.
- W2891483162 modified "2023-09-27" @default.
- W2891483162 title "HESCNET: A Synthetically Pre-Trained Convolutional Neural Network for Human Embryonic Stem Cell Colony Classification" @default.
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- W2891483162 doi "https://doi.org/10.1109/icip.2018.8451624" @default.
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