  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.
