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- W4296194903 abstract "In this paper, we present a deep learning-based framework for automated analysis and diagnosis of Cryptosporidium parvum from fluorescence microscopic images. First, a coarse segmentation is applied to roughly delimit the contours either of individual parasites or of grouped ones in the form of a single object from original images. Subsequently, a classifier will be applied to identify grouped parasites which are separated from each other by applying a fine segmentation. Our coarse-to-fine segmentation methodology achieves high accuracy on our generated dataset (over 3,000 parasites) and permit to improve the performance of direct segmentation approaches." @default.
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- W4296194903 date "2022-01-01" @default.
- W4296194903 modified "2023-10-16" @default.
- W4296194903 title "A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of Cryptosporidium Parasite from Fluorescence Microscopic Images" @default.
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- W4296194903 doi "https://doi.org/10.1007/978-3-031-16961-8_16" @default.
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