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• W3094590434 abstract "During the process of whole slide imaging, it is necessary to focus thousands of fields of view to obtain a high-quality image. To make the focusing procedure efficient and effective, we propose a novel autofocus algorithm for whole slide imaging. It is based on convolution and recurrent neural networks to predict the out-of-focus distance and subsequently update the focus location of the camera lens in an iterative manner. More specifically, we train a convolution neural network to extract focus information in the form of a focus feature vector. In order to make the prediction more accurate, we apply a recurrent neural network to combine focus information from previous search iteration and current search iteration to form a feature aggregation vector. This vector contains more focus information than the previous one and is subsequently used to predict the out-of-focus distance. Our experiments indicate that our proposed autofocus algorithm is able to rapidly determine the optimal in-focus image. The code is available at https://github.com/hezhujun/autofocus-rnn." @default.
• W3094590434 created "2020-10-29" @default.
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• W3094590434 date "2021-01-01" @default.
• W3094590434 modified "2023-10-15" @default.
• W3094590434 title "Autofocus of whole slide imaging based on convolution and recurrent neural networks" @default.
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• W3094590434 doi "https://doi.org/10.1016/j.ultramic.2020.113146" @default.
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