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首页> 外文会议>IEEE Conference on Computer Vision and Pattern Recognition Workshops >High-Magnification Multi-views Based Classification of Breast Fine Needle Aspiration Cytology Cell Samples Using Fusion of Decisions from Deep Convolutional Networks
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High-Magnification Multi-views Based Classification of Breast Fine Needle Aspiration Cytology Cell Samples Using Fusion of Decisions from Deep Convolutional Networks

机译：基于高倍率的基于多视图的乳房细针抽吸细胞学细胞样品的分类，使用来自深度卷积网络的决定的融合

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摘要

Fine needle aspiration cytology is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a deep convolutional neural network (CNN) based classification approach for the diagnosis of the cell samples using their microscopic high-magnification multi-views. The proposed approach has been tested using GoogLeNet architecture of CNN on an image dataset of 37 breast cytopathology samples (24 benign and 13 malignant), where the network was trained using images of ~54% cell samples and tested on the rest, achieving 89.7% mean accuracy in 8 fold validation.
机译：细针穿刺细胞学通常用于诊断乳腺癌，具有传统实践，基于显微镜下乳腺细胞病变细胞样品的主观视觉评估，评估各种细胞学特征的状态。因此，在保持调查结果的一致性和再现性方面存在许多挑战。然而，数字成像和诊断的计算辅助可以提高诊断准确性并减少病理学家的有效工作量。本文介绍了一种基于深度卷积神经网络（CNN）的分类方法，用于使用它们的微观高倍多视图诊断细胞样本。在37个乳腺细胞病变样本（24良性和13个恶性）的图像数据集上使用CNN的Googlenet架构进行了测试，其中网络使用〜54％的细胞样品的图像进行培训并在其余的情况下进行测试，实现89.7 ％均匀的验证效率为8倍。

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