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- W2969640968 abstract "The aim of this study is the comparison of the various deep convolutional neural networks for segmentation of fibroblast in brightfield microscopic images. This investigation compares two main architectures: Unet and Linknet. Every main architecture is equipped with various ‘backbone’ network creating specific bundle. The experimental dataset consisting of 16 sequences of images of monitored cells’ culture have been split into training and validation set. Then it was analysed and used for validation of the networks to establish the best bundle (net architecture and ‘backbone’). This study proved that trained deep convolutional neural networks could be used as a segmentation tool in this task." @default.
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- W2969640968 date "2019-08-23" @default.
- W2969640968 modified "2023-09-27" @default.
- W2969640968 title "Fibroblast Segmentation in Microscopic Brightfield Images with Convolutional Neural Network" @default.
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- W2969640968 doi "https://doi.org/10.1007/978-3-030-29885-2_13" @default.
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