基于注意力机制与迁移学习的乳腺钼靶肿块分类

针对乳腺钼靶图像中良恶性肿块难以诊断的问题,提出一种基于注意力机制与迁移学习的乳腺钼靶肿块分类方法,并用于医学影像中乳腺钼靶肿块的良恶性分类。首先,构建一种新的网络模型,该模型将注意力机制CBAM(Convolutional Block Attention Module)与残差网络ResNet50相结合,用于提高网络对肿块病变特征的提取能力,增强特定语义的特征表示。其次,提出一种新的迁移学习方法,用切片数据集代替传统方法中作为迁移学习源域的ImageNet,完成局部肿块切片到全局乳腺图片的领域自适应学习,可用于提升网络对细节病理特征的感知能力。实验结果表明,所提方法在局部乳腺肿块切片数据集和全局乳腺钼靶数据集上的AUC(Area Under Receiver Operating Characteristics Curve)分别达到0.8607和0.8081。结果证实本文分类方法的有效性。

Mass Classification of Breast Mammogram Based on Attention Mechanism and Transfer Learning

This study aimed to address issues of difficult diagnosis of benign and malignant masses in breast mammogram.For medical imaging,this study proposed a classification method of benign and malignant masses in breast mammogram based on attention mechanism and transfer learning.First,a new network model was built by combining convolutional block attention module(CBAM)and the residual network ResNet50 to improve the ability of the network to extract the features of the mass lesions and enhance specific semantic feature representation.Then,a new transfer learning method was proposed;instead of traditional method using the ImageNet as the transfer learning source domain,the patch data were used as the transfer learning source domain to complete the domain adaptive learning from local mass patch images to global breast mammogram,which can improve the ability of the network to capture pathological features.The experimental results show that the proposed method achieves an area under the receiver operating characteristics curve(AUC)value of 0.8607 in the local breast mass patch dataset and an AUC value of 0.8081 in the global breast mammogram dataset.The results confirm the effectiveness of the proposed classification method.