It is of great significance to diagnose Invasive Ductal Carcinoma (IDC) in early stage, which is the most common subtype of breast cancer. Although the powerful models in the Computer-Aided Diagnosis (CAD) systems provide promising results, it is still difficult to integrate them into other medical devices or use them without sufficient computation resource. In this paper, we propose BCDNet, which firstly upsamples the input image by the residual block and use smaller convolutional block and a special MLP to learn features. BCDNet is proofed to effectively detect IDC in histopathological RGB images with an average accuracy of 91.6% and reduce training consumption effectively compared to ResNet 50 and ViT-B-16.

翻译：早期诊断浸润性导管癌（IDC）具有重要意义，这是乳腺癌最常见的亚型。尽管计算机辅助诊断（CAD）系统中的强大模型提供了有希望的结果，但将其集成到其他医疗设备中或在没有足够计算资源的情况下使用仍然很困难。本文提出BCDNet，该网络首先通过残差块对输入图像进行上采样，并使用较小的卷积块和特殊的MLP来学习特征。经证明，BCDNet能有效检测组织病理学RGB图像中的IDC，平均准确率达到91.6%，并且相较于ResNet 50和ViT-B-16，显著降低了训练消耗。