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    • 基于深度学习的肾小球病理图像识别与分类

    Meng Ziyao,Chen Sijia, Lyu Tianyu,Zhang Zhigang,Wang Xiaoxia,Sheng Bin,Mao Lijuan

    Journal of Computer-aided Design & Computer Graphics（2021）

    Cited 4|Views14
    Abstract
    病理切片中肾小球的识别和分类是诊断肾脏病变程度和病变类型的关键,为解决肾小球的识别和分类问题,从中检测出肾小球并进行分类,设计了一个基于深度学习的完整的肾小球检测及分类框架.该框架包括肾小球识别的4个阶段,第1阶段的扫描窗生成中,设计一种网络框架RGNet,用于初步判断肾小球可能出现的位置;第2阶段的检测和粗分类中,针对肾小球数据改进了Faster R-CNN;第3阶段基于NMS算法设计了NMS-Lite算法,将检测到的肾小球进行合并;在第4阶段的细分类中,使用数据增强等技巧训练2个神经网络,实现肾小球的病变程度分类.实验结果表明,所提肾小球检测方法在测试集上取得了与同类方法可比的精度,且在一定程度上解决了相似类别的肾小球难以区分的问题.
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
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    【要点】：本文提出了一种基于深度学习的肾小球识别与分类框架，有效解决了肾小球病变程度的诊断问题，并在检测精度上取得了与现有方法相当的效果。
    

    【方法】：文章设计了一种包括四个阶段的框架，分别为扫描窗口生成、检测与粗分类、检测合并、细分类，其中采用了自定义的RGNet网络、改进的Faster R-CNN算法、NMS-Lite算法以及数据增强技术。
    

    【实验】：作者在测试集上验证了所提方法，使用的数据集未明确提及，但实验结果表明该方法的肾小球检测准确度与现有方法相当，并能较好地分辨相似类型的肾小球。