College of Medicine and Biological Information Engineering, Northeastern University, Shengyang, China.
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA.
Sci Rep. 2022 Aug 30;12(1):14720. doi: 10.1038/s41598-022-18747-y.
The survival rate of breast cancer patients is closely related to the pathological stage of cancer. The earlier the pathological stage, the higher the survival rate. Breast ultrasound is a commonly used breast cancer screening or diagnosis method, with simple operation, no ionizing radiation, and real-time imaging. However, ultrasound also has the disadvantages of high noise, strong artifacts, low contrast between tissue structures, which affect the effective screening of breast cancer. Therefore, we propose a deep learning based breast ultrasound detection system to assist doctors in the diagnosis of breast cancer. The system implements the automatic localization of breast cancer lesions and the diagnosis of benign and malignant lesions. The method consists of two steps: 1. Contrast enhancement of breast ultrasound images using segmentation-based enhancement methods. 2. An anchor-free network was used to detect and classify breast lesions. Our proposed method achieves a mean average precision (mAP) of 0.902 on the datasets used in our experiment. In detecting benign and malignant tumors, precision is 0.917 and 0.888, and recall is 0.980 and 0.963, respectively. Our proposed method outperforms other image enhancement methods and an anchor-based detection method. We propose a breast ultrasound image detection system for breast cancer detection. The system can locate and diagnose benign and malignant breast lesions. The test results on single dataset and mixed dataset show that the proposed method has good performance.
乳腺癌患者的生存率与癌症的病理分期密切相关。病理分期越早，生存率越高。乳腺超声是一种常用的乳腺癌筛查或诊断方法，操作简单，无电离辐射，实时成像。然而，超声也有操作噪音大、伪影强、组织结构对比度低等缺点，影响乳腺癌的有效筛查。因此，我们提出了一种基于深度学习的乳腺超声检测系统，以辅助医生诊断乳腺癌。该系统实现了乳腺癌病变的自动定位和良性恶性病变的诊断。该方法包括两个步骤：1. 基于分割的增强方法对乳腺超声图像进行对比度增强。2. 采用无锚点网络进行乳腺病变的检测和分类。我们提出的方法在实验中使用的数据集上实现了 0.902 的平均精度（mAP）。在检测良性和恶性肿瘤时，精度分别为 0.917 和 0.888，召回率分别为 0.980 和 0.963。我们提出的方法优于其他图像增强方法和基于锚点的检测方法。我们提出了一种用于乳腺癌检测的乳腺超声图像检测系统。该系统可以定位和诊断良性和恶性乳腺病变。在单一数据集和混合数据集上的测试结果表明，该方法具有良好的性能。
