Wang Wei, Wang Yisong
College of Computer Science and Technology, Guizhou University, Guiyang 550001, China.
Institute for Artificial Intelligence, Guizhou University, Guiyang 550001, China.
Diagnostics (Basel). 2023 Apr 28;13(9):1582. doi: 10.3390/diagnostics13091582.
Computer-aided methods have been extensively applied for diagnosing breast lesions with magnetic resonance imaging (MRI), but fully-automatic diagnosis using deep learning is rarely documented. Deep-learning-technology-based artificial intelligence (AI) was used in this work to classify and diagnose breast cancer based on MRI images. Breast cancer MRI images from the Rider Breast MRI public dataset were converted into processable joint photographic expert group (JPG) format images. The location and shape of the lesion area were labeled using the Labelme software. A difficult-sample mining mechanism was introduced to improve the performance of the YOLACT algorithm model as a modified YOLACT algorithm model. Diagnostic efficacy was compared with the Mask R-CNN algorithm model. The deep learning framework was based on PyTorch version 1.0. Four thousand and four hundred labeled data with corresponding lesions were labeled as normal samples, and 1600 images with blurred lesion areas as difficult samples. The modified YOLACT algorithm model achieved higher accuracy and better classification performance than the YOLACT model. The detection accuracy of the modified YOLACT algorithm model with the difficult-sample-mining mechanism is improved by nearly 3% for common and difficult sample images. Compared with Mask R-CNN, it is still faster in running speed, and the difference in recognition accuracy is not obvious. The modified YOLACT algorithm had a classification accuracy of 98.5% for the common sample test set and 93.6% for difficult samples. We constructed a modified YOLACT algorithm model, which is superior to the YOLACT algorithm model in diagnosis and classification accuracy.
计算机辅助方法已被广泛应用于利用磁共振成像（MRI）诊断乳腺病变，但使用深度学习进行全自动诊断的相关记录却很少。本研究使用基于深度学习技术的人工智能（AI），基于MRI图像对乳腺癌进行分类和诊断。将来自Rider Breast MRI公共数据集的乳腺癌MRI图像转换为可处理的联合图像专家组（JPG）格式图像。使用Labelme软件标记病变区域的位置和形状。引入了难样本挖掘机制，以改进作为改进型YOLACT算法模型的YOLACT算法模型的性能。将诊断效能与Mask R-CNN算法模型进行比较。深度学习框架基于PyTorch 1.0版本。4400个带有相应病变的标记数据被标记为正常样本，1600个病变区域模糊的图像被标记为难样本。改进后的YOLACT算法模型比YOLACT模型具有更高的准确率和更好的分类性能。对于常见和难样本图像，带有难样本挖掘机制的改进型YOLACT算法模型的检测准确率提高了近3%。与Mask R-CNN相比，其运行速度仍然更快，识别准确率的差异不明显。改进后的YOLACT算法对常见样本测试集的分类准确率为98.5%，对难样本的分类准确率为93.6%。我们构建了一种改进型YOLACT算法模型，其在诊断和分类准确率方面优于YOLACT算法模型。
