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- W4312298039 abstract "Deep learning has proven to be an effective approach to read whole-slide cytologic images for computer-assisted cervical cancer screening. To construct such a pipeline, it is often necessary to train a classifier, which decides if a patch (for example, sized $$224times 224$$ ) provides a positive cell reading or not. Following the clinical guidance, pathologists must label many such patches of both negative (N) and positive (P) cells. Then, a deep network can be trained for the binary N-P classification problem. In this paper, we take advantage of the complexity and illegibility of the cells that are negative according to current clinical guidance, to further improve the classification performance. We claim that the negative patches can be divided into two types: easy-negative (EN) and hard-negative (HN). The initial N-P binary classification can then be converted to an EN-HN-P triple-class problem. We also align the EN-HN and HN-P decision planes in parallel in the latent feature space where all input patches are encoded. The dual planes perform parallel classification then, following a well-planned curriculum learning scheme. Our results show that the proposed method can greatly enhance the performance of classifying positive patches and negative patches by using the better learned latent space and the related encoding of each patch." @default.
- W4312298039 created "2023-01-04" @default.
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- W4312298039 date "2022-01-01" @default.
- W4312298039 modified "2023-10-14" @default.
- W4312298039 title "Parallel Classification of Cells in Thinprep Cytology Test Image for Cervical Cancer Screening" @default.
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- W4312298039 doi "https://doi.org/10.1007/978-3-031-17979-2_4" @default.
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