Vision mamba for classification of breast ultrasound images
Published in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Deep-Brea3th Workshop, 2024
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 dataset and Breast Ultrasound B dataset. Our evaluation, which includes multiple runs of experiments and statistical significance analysis, demonstrates that some of the Mamba-based architectures often outperform CNN and ViT models with statistically significant results. For example, in the B dataset, the best Mamba-based models have a 1.98\% average AUC and a 5.0\% average Accuracy improvement compared to the best non-Mamba-based model in this study. These Mamba-based models effectively capture long-range dependencies while maintaining some inductive biases, making them suitable for applications with limited data.
Recommended citation: A. Nasiri-Sarvi, M. S. Hosseini, and H. Rivaz, “Vision mamba for classification of breast ultrasound images,” arXiv preprint arXiv:2407.03552, 2024.
Download Paper