Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
复制标题
使用卷积神经网络确定超声图像乳腺病变组织学分类的计算机辅助诊断方案
作者：
A. Hizukuri;R. Nakayama
临床医生很难在超声图像上准确区分乳腺病变的组织学分类。本研究的目的是开发一种使用卷积神经网络(CNN)来确定乳腺病变的组织学分类的计算机辅助诊断(CADx)方案。我们的数据库包括578张乳腺超声图像。恶性病变287例，其中浸润性癌217例，非浸润性癌70例；良性病变291例，囊性病变111例，纤维腺瘤180例。在这项研究中，使用由四个卷积层、三个批次归一化层、四个汇集层和两个完全连通层构成的CNN来区分四种不同类型的病变组织学分类。我们的CNN模型对组织学分类的分类准确率为83.9-87.6%，大大高于我们以前使用手工特征和分类器的方法(55.7-79.3%)。我们的CNN模型的曲线下面积为0.976，而我们以前的方法的曲线下面积为0.939(p=0.0001)。我们的CNN模型在乳腺病变的鉴别诊断中将是有用的，作为诊断辅助。
It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our database consisted of 578 breast ultrasonographic images. It included 287 malignant (217 invasive carcinomas and 70 noninvasive carcinomas) and 291 benign lesions (111 cysts and 180 fibroadenomas). In this study, the CNN constructed from four convolutional layers, three batch-normalization layers, four pooling layers, and two fully connected layers was employed for distinguishing between the four different types of histological classifications for lesions. The classification accuracies for histological classifications with our CNN model were 83.9–87.6%, which were substantially higher than those with our previous method (55.7–79.3%) using hand-crafted features and a classifier. The area under the curve with our CNN model was 0.976, whereas that with our previous method was 0.939 (p = 0.0001). Our CNN model would be useful in differential diagnoses of breast lesions as a diagnostic aid.
影响因子：
6.7
作者：
DORFMAN, DD;BERBAUM, KS;METZ, CE
通讯作者：
METZ, CE
影响因子：
120.7
作者：
Berg, Wendie A.;Blume, Jeffrey D.;Boparai, Karan
通讯作者：
Boparai, Karan