DCT-HistoTransformer：融合DCT的高效轻量级视觉Transformer用于组织病理图像分析
DCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis
AI总结：
本文提出DCT-HistoTransformer，通过DCT注意力与MobileConv并行通路将图像转换到频域，实现无需大型数据集的轻量级乳腺癌组织病理分类，在二分类和多分类上分别达到96.00%和87.85%的准确率，显著降低计算成本。
摘要：
近年来，先进成像技术与深度学习方法的融合显著推动了用于乳腺癌检测与分类的计算机辅助诊断（CAD）系统的发展。Transformer在计算机视觉领域展现出巨大潜力，目前正被应用于医学图像分析。然而，将其应用于组织病理图像面临挑战，因为需要对全切片图像（WSIs）进行大量人工标注，而这些模型需要大量数据才能有效工作，成本高且耗时。此外，Vision Transformer（ViTs）的二次计算成本对于大型高分辨率组织病理图像尤其难以承受，尤其是在计算资源有限的边缘设备上。在本研究中，我们提出了一种新颖的基于Transformer的轻量级乳腺癌分类方法，无需大型数据集即可有效运行。通过引入离散余弦变换（DCT）注意力与MobileConv的并行处理通路，我们将图像数据从空间域转换到频域，以利用诸如滤除图像中的高频成分等优势，从而降低计算成本。我们提出的模型在二分类任务中达到96.00% ± 0.48%的准确率，在多分类任务中达到87.85% ± 0.93%的准确率，与最先进模型相当，同时显著降低了计算成本。这表明我们的方法在改善组织病理图像乳腺癌分类方面具有潜力，提供了一种更高效的解决方案，减少了对大规模标注数据集的依赖。
英文摘要：
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.
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