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Image Classification by Fusion for High-Content Cell-Cycle Screening

机译:融合的图像分类用于高内涵细胞周期筛选

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摘要

We present a fuzzy fusion approach for combining cell-phase identification results obtained from multiple classifiers. This approach can improve the classification rates and allows the task of high-content cell-cycle screening more effective for biomedical research in the study of structures and functions of cells and molecules. Conventionally such study requires the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poorly reproducible. The proposed method has been used to combine the results from three classifiers, and the combined result is superior to any of the results obtained from a single classifier.
机译:我们提出了一种模糊融合方法,用于组合从多个分类器获得的细胞相识别结果。这种方法可以提高分类率,并使高含量的细胞周期筛选任务对于生物医学研究中细胞和分子的结构和功能研究更为有效。传统上,这种研究需要处理和分析大量的图像数据,并且手动图像分析非常耗时,因此成本高昂,并且还可能不准确且再现性差。所提出的方法已被用于合并来自三个分类器的结果,并且合并后的结果优于从单个分类器获得的任何结果。

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