利用卷积神经网络的深度学习方法在乳腺超声中区分良恶性乳腺肿块。
Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.
机构信息
Department of Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo, Japan.
出版信息
Jpn J Radiol. 2019 Jun;37(6):466-472. doi: 10.1007/s11604-019-00831-5. Epub 2019 Mar 19.
PURPOSE
We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.
MATERIALS AND METHODS
We retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. Deep learning model was constructed using CNN architecture GoogLeNet and analyzed test data: 48 benign masses, 72 malignant masses. Three radiologists interpreted these test data. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.
RESULTS
The CNN model and radiologists had a sensitivity of 0.958 and 0.583-0.917, specificity of 0.925 and 0.604-0.771, and accuracy of 0.925 and 0.658-0.792, respectively. The CNN model had equal or better diagnostic performance compared to radiologists (AUC = 0.913 and 0.728-0.845, p = 0.01-0.14).
CONCLUSION
Deep learning with CNN shows high diagnostic performance to discriminate between benign and malignant breast masses on ultrasound.
目的
我们旨在利用深度学习卷积神经网络（CNN）来区分超声乳腺肿块的良恶性。
材料与方法
我们回顾性地收集了 96 个良性肿块的 480 个图像和 144 个恶性肿块的 467 个图像作为训练数据。使用 CNN 架构 GoogLeNet 构建深度学习模型，并分析测试数据：48 个良性肿块，72 个恶性肿块。三位放射科医生对这些测试数据进行解读。计算了敏感性、特异性、准确性和接收器工作特征曲线（ROC）下的面积（AUC）。
结果
CNN 模型和放射科医生的敏感性分别为 0.958 和 0.583-0.917，特异性分别为 0.925 和 0.604-0.771，准确性分别为 0.925 和 0.658-0.792。CNN 模型的诊断性能与放射科医生相当或更好（AUC 分别为 0.913 和 0.728-0.845，p 值分别为 0.01-0.14）。
结论
基于深度学习的 CNN 在区分超声乳腺良恶性肿块方面具有较高的诊断性能。
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