  Mamba-based models, VMamba and Vim, are a recent family of vision encoders
that offer promising performance improvements in many computer vision tasks.
This paper compares Mamba-based models with traditional Convolutional Neural
Networks (CNNs) and Vision Transformers (ViTs) using the breast ultrasound BUSI
and B datasets. Our evaluation, which includes multiple runs of experiments and
statistical significance analysis, demonstrates that Mamba-based architectures
frequently outperform CNN and ViT models with statistically significant
results. These Mamba-based models effectively capture long-range dependencies
while maintaining inductive biases, making them suitable for applications with
limited data.
