Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging
Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging
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DOI：
10.3390/diagnostics10070456
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发表时间：
2020-07-01
期刊：
影响因子：
3.6
通讯作者：
Tateishi, Ukihide
中科院分区：
文献类型：
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作者：
Fujioka, Tomoyuki;Kubota, Kazunori;Tateishi, Ukihide
我们的目的是使用生成对抗网络（GAN）为基础的异常检测来诊断乳腺超声图像上的正常组织、良性肿块或恶性肿块。我们回顾性收集了69例患者的531张正常乳腺超声图像。进行了数据增强，有6372 （531 x 12）张图像可用于训练。利用高效的基于gan的异常检测构建计算模型，检测图像中的异常病灶，并计算异常作为异常评分。我们分析了51个正常组织、48个良性肿块和72个恶性肿块的图像作为测试数据。计算了该异常检测模型的灵敏度、特异度和接收者工作特征曲线下面积（AUC）。恶性肿块异常评分明显高于良性肿块（p< 0.001），良性肿块异常评分明显高于正常组织（p< 0.001）。我们的异常检测模型在区分正常组织与良恶性肿块方面具有较高的灵敏度、特异性和AUC值，在区分正常组织与恶性肿块方面具有更高的值。基于gan的异常检测在乳腺超声图像异常病灶的检测和诊断中表现出较高的性能。
We aimed to use generative adversarial network (GAN)-based anomaly detection to diagnose images of normal tissue, benign masses, or malignant masses on breast ultrasound. We retrospectively collected 531 normal breast ultrasound images from 69 patients. Data augmentation was performed and 6372 (531 x 12) images were available for training. Efficient GAN-based anomaly detection was used to construct a computational model to detect anomalous lesions in images and calculate abnormalities as an anomaly score. Images of 51 normal tissues, 48 benign masses, and 72 malignant masses were analyzed for the test data. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of this anomaly detection model were calculated. Malignant masses had significantly higher anomaly scores than benign masses (p< 0.001), and benign masses had significantly higher scores than normal tissues (p< 0.001). Our anomaly detection model had high sensitivities, specificities, and AUC values for distinguishing normal tissues from benign and malignant masses, with even greater values for distinguishing normal tissues from malignant masses. GAN-based anomaly detection shows high performance for the detection and diagnosis of anomalous lesions in breast ultrasound images.