In recent years, the integration of advanced imaging techniques and deep learning methods has significantly advanced computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Transformers, which have shown great promise in computer vision, are now being applied to medical image analysis. However, their application to histopathological images presents challenges due to the need for extensive manual annotations of whole-slide images (WSIs), as these models require large amounts of data to work effectively, which is costly and time-consuming. Furthermore, the quadratic computational cost of Vision Transformers (ViTs) is particularly prohibitive for large, high-resolution histopathological images, especially on edge devices with limited computational resources. In this study, we introduce a novel lightweight breast cancer classification approach using transformers that operates effectively without large datasets. By incorporating parallel processing pathways for Discrete Cosine Transform (DCT) Attention and MobileConv, we convert image data from the spatial domain to the frequency domain to utilize the benefits such as filtering out high frequencies in the image, which reduces computational cost. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets. Our proposed model achieves an accuracy of 96.00% $\pm$ 0.48% for binary classification and 87.85% $\pm$ 0.93% for multiclass classification, which is comparable to state-of-the-art models while significantly reducing computational costs. This demonstrates the potential of our approach to improve breast cancer classification in histopathological images, offering a more efficient solution with reduced reliance on extensive annotated datasets.

本研究针对传统视觉变换器在组织病理图像分析中的应用面临的数据标注成本高及计算负担重的问题，提出了一种新颖的轻量级乳腺癌分类方法。通过结合离散余弦变换注意力和MobileConv的并行处理途径，我们显著提升了分类效率和准确性，模型在二分类任务中达到96.00%的准确率，展示了在减少对大量标注数据依赖的同时，仍能实现优良的分类性能。

高效轻量级视觉变换器DCT-HistoTransformer及其在组织病理图像分析中的应用