Vision mamba for classification of breast ultrasound images
Accepted in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Deep-Brea3th Workshop, 2024
This paper shows that Mamba-based models outperform CNNs and ViTs on breast ultrasound datasets, achieving a 1.98% AUC and 5.0% accuracy improvement, while effectively capturing long-range dependencies.
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.
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