Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images. The inability of CNNs to generalize across magnification scales can result in sub-optimal performance on external datasets. This study aims to evaluate the robustness of various deep learning architectures in the analysis of breast cancer histopathological images with varying magnification scales at training and testing stages. Here we explore and compare the performance of multiple deep learning architectures, including CNN-based ResNet and MobileNet, self-attention-based Vision Transformers and Swin Transformers, and token-mixing models, such as FNet, ConvMixer, MLP-Mixer, and WaveMix. The experiments are conducted using the BreakHis dataset, which contains breast cancer histopathological images at varying magnification levels. We show that performance of WaveMix is invariant to the magnification of training and testing data and can provide stable and good classification accuracy. These evaluations are critical in identifying deep learning architectures that can robustly handle changes in magnification scale, ensuring that scale changes across anatomical structures do not disturb the inference results.

翻译：卷积神经网络（CNN）广泛用于医学图像分析，但当测试图像的放大倍数与训练图像不同时，其性能会显著下降。CNN无法跨放大尺度泛化，可能导致在外数据集上表现欠佳。本研究旨在评估不同深度学习架构在训练和测试阶段处理不同放大尺度乳腺癌组织病理图像时的鲁棒性。我们探索并比较了多种深度学习架构的性能，包括基于CNN的ResNet和MobileNet、基于自注意力的视觉Transformer和Swin Transformer，以及Token混合模型（如FNet、ConvMixer、MLP-Mixer和WaveMix）。实验采用包含不同放大倍数乳腺癌组织病理图像的BreakHis数据集进行。结果表明，WaveMix的性能对训练和测试数据的放大倍数具有不变性，能够提供稳定且良好的分类准确率。这些评估对于识别能稳健处理放大尺度变化的深度学习架构至关重要，确保解剖结构尺度变化不会干扰推理结果。