一种乳腺X线影像肿块的多特征融合检测算法

A mutil-feature fusion algorithm for Mammography masses

针对在单视图的乳腺肿块检测算法中漏检率和假阳性率较高的问题,提出了一种改进的自动检测算法。将扩张残留网络(Dilated Residual Network,DRN)结合重新设计的特征金字塔网络(Feature Pyramid Network,FPN)用于对乳腺肿块的检测。首先利用DRN中的膨胀卷积,减少对图像的下采样次数;再扩充网络的深度,使其输出满足FPN所需的输入;在FPN结构中,采用注意力机制降低不同特征图直接融合所造成的信息损失,同时采用密集连接代替原有的横向连接,充分融合浅层特征中目标的位置和细节信息。仿真实验显示,所设计的模型在CBSI-DDSM数据集上的检测精度相比于基准模型提升了7.1%。

Aiming at the problem of the high probability of miss and false positive rates in single-view Mammography,an improved automatic detection algorithm is proposed in this paper.The dilated residual network(DRN)combined with a modified feature pyramid network(FPN)is used for the detection of breast masses.The expansion convolution in DRN is used to reduce the number of downsampling of images.The number of layers of the DRN is also increased to satisfy the required input of the FPN.In the FPN structure,the attention mechanism is used to reduce the information loss caused by the direct fusion of different feature maps,while dense connections are used instead of the original lateral connections to make full use of the location and detailed information on the target for the shallow features.Simulation experiments show that the detection accuracy of the designed model on the CBSI-DDSM dataset is improved by 7.1 percent compared to the baseline.