Vision Mamba for Classification of Breast Ultrasound Images
- URL: http://arxiv.org/abs/2407.03552v1
- Date: Thu, 4 Jul 2024 00:21:47 GMT
- Title: Vision Mamba for Classification of Breast Ultrasound Images
- Authors: Ali Nasiri-Sarvi, Mahdi S. Hosseini, Hassan Rivaz,
- Abstract summary: 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.
- Score: 9.90112908284836
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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