Computer-aided breast mass classification is an effective and widely used technology to assist pathologists in formulating clinical diagnoses and improving working efficiencies. Existing studies usually use a single image feature to perform breast mass classification. Herein, we propose a simple, yet effective, model called the DE-Ada*, which is an organic integration of multi-feature fusions, for breast mass classification. Firstly, we extract a set of complementary features, namely, scale-invariant feature transform (SIFT), GIST, histogram of oriented gradient (HOG), local binary pattern (LBP), residual network (ResNet), densely connected convolutional networks (DenseNet), and visual geometry group (VGG), to characterize mammograms from diverse perspectives. We attempt to mine the cross-modal pathological semantics among these features and complete their early fusion. The dynamic weight of any feature or cross-modal pathological semantics is computed and utilized to complete mid-level feature fusion. Finally, we design two voting-based ensemble learning strategies to implement late feature fusion. Our experiments demonstrate that the DE-Ada* model outperforms baselines on two well-known mammographic datasets. Our model encourages the use of cross-modal pathological semantics to deal with the overfitting problem.

计算机辅助乳腺肿块分类是一种有效且广泛使用的技术，可以帮助病理学家制定临床诊断并提高工作效率。现有研究通常使用单个图像特征进行乳房质量分类。本文中，我们提出了一种简单而有效的模型，称为DE-Ada *，该模型是多特征融合的有机结合，用于乳房质量分类。首先，我们提取一组互补特征，即尺度不变特征变换（SIFT），GIST，定向梯度直方图（HOG），局部二值模式（LBP），残差网络（ResNet），密集连接的卷积网络（DenseNet ）和视觉几何组（VGG），以从不同角度表征乳房X光照片。我们试图挖掘这些特征之间的交叉模式病理语义，并完成它们的早期融合。计算任何特征或跨模式病理语义的动态权重，并将其用于完成中级特征融合。最后，我们设计了两种基于投票的整体学习策略来实现后期特征融合。我们的实验表明，DE-Ada *模型在两个著名的乳腺摄影数据集上的表现优于基线。我们的模型鼓励使用交叉模式病理语义来处理过度拟合问题。我们的实验表明，DE-Ada *模型在两个著名的乳腺摄影数据集上的表现优于基线。我们的模型鼓励使用交叉模式病理语义来处理过度拟合问题。我们的实验表明，DE-Ada *模型在两个著名的乳腺摄影数据集上的表现优于基线。我们的模型鼓励使用交叉模式病理语义来处理过度拟合问题。