Abstract
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 × 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.
Highlights
Breast cancer is the most common cancer and the second leading cause of cancer death among women [1]
The most common histopathology was fibroadenoma in patients with benign masses and invasive ductal carcinoma in Malignant masses were significantly larger than benign masses, and patients with malignant masses were significantly older than those with benign masses (p < 0.001; Table 1)
We focused on the detection and differential diagnosis of normal, benign, In the present study, we focused on the detection and differential diagnosis of normal, benign, and and malignant breast tissues ultrasound images using efficient
Summary
Breast cancer is the most common cancer and the second leading cause of cancer death among women [1]. Ultrasound is a widely used modality for detecting and diagnosing breast cancer when other imaging modalities such as mammography and clinical examination find abnormalities. Ultrasound is considered a leading imaging modality because of its high availability, cost effectiveness, acceptable diagnostic performance, and noninvasive real-time capabilities [2,3,4]. The breast imaging reporting and data system lexicon [5] was developed by the American. College of Radiology to standardize terms for the description and classification of breast lesions and was reported to show good diagnostic performance. The diagnosis of images relies on the experience of radiologists. Significant intra- and inter-individual variabilities may Diagnostics 2020, 10, 456; doi:10.3390/diagnostics10070456 www.mdpi.com/journal/diagnostics
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