Since the impressive superior performance demonstrated by deep learning methods is widely used in histopathological image analysis and diagnosis, existing work cannot fully extract the information in the breast cancer images due to the limited high resolution of histopathological images. In this study, we construct a novel intermediate layer structure that fully extracts feature information and name it DMBANet, which can extract as much feature information as possible from the input image by up-dimensioning the intermediate convolutional layers to improve the performance of the network. Furthermore, we employ the depth-separable convolution method on the Spindle Structure by decoupling the intermediate convolutional layers and convolving them separately, to significantly reduce the number of parameters and computation of the Spindle Structure and improve the overall network operation speed. We also design the Spindle Structure as a multi-branch model and add different attention mechanisms to different branches. Spindle Structure can effectively improve the performance of the network, the branches with added attention can extract richer and more focused feature information, and the branch with residual connections can minimize the degradation phenomenon in our network and speed up network optimization. The comprehensive experiment shows the superior performance of DMBANet compared to the state-of-the-art method, achieving about 98% classification accuracy, which is better than existing methods. The code is available at https://github.com/Nagi-Dr/DMBANet-main.

由于深度学习方法所表现出的令人印象深刻的优越性能被广泛应用于组织病理学图像分析和诊断，但由于组织病理学图像的高分辨率有限，现有工作无法充分提取乳腺癌图像中的信息。在本研究中，我们构建了一种新颖的完全提取特征信息的中间层结构，并将其命名为DMBANet，它可以通过对中间卷积层进行升维来从输入图像中提取尽可能多的特征信息，以提高网络的性能。此外，我们在Spindle结构上采用深度可分离卷积方法，通过解耦中间卷积层并单独进行卷积，显着减少Spindle结构的参数数量和计算量，提高整体网络运行速度。我们还将Spindle Structure设计为多分支模型，并对不同分支添加不同的注意力机制。主轴结构可以有效提高网络的性能，添加注意力的分支可以提取更丰富、更集中的特征信息，而具有剩余连接的分支可以最大限度地减少网络中的退化现象并加快网络优化速度。综合实验表明，与最先进的方法相比，DMBANet 具有优越的性能，实现了约 98% 的分类准确率，优于现有方法。该代码可从 https://github.com/Nagi-Dr/DMBANet-main 获取。