High-Magnification Multi-views Based Classification of Breast Fine Needle Aspiration Cytology Cell Samples Using Fusion of Decisions from Deep Convolutional Networks.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops(2017)

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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.
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关键词
breast cytopathology samples,GoogLeNet architecture,microscopic high-magnification multiviews,CNN,breast cytopathology cell,subjective visual assessment,breast cancer diagnosis,decisions fusion,deep convolutional networks,breast fine needle aspiration cytology cell samples,high-magnification multiviews based classification
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