MedMamba: Vision Mamba for Medical Image Classification
CoRR(2024)
摘要
Medical image classification is a very fundamental and crucial task in the
field of computer vision. These years, CNN-based and Transformer-based models
are widely used in classifying various medical images. Unfortunately, The
limitation of CNNs in long-range modeling capabilities prevent them from
effectively extracting fine-grained features in medical images , while
Transformers are hampered by their quadratic computational complexity. Recent
research has shown that the state space model (SSM) represented by Mamba can
efficiently model long-range interactions while maintaining linear
computational complexity. Inspired by this, we propose Vision Mamba for medical
image classification (MedMamba). More specifically, we introduce a novel
Conv-SSM module, which combines the local feature extraction ability of
convolutional layers with the ability of SSM to capture long-range dependency.
To demonstrate the potential of MedMamba, we conduct extensive experiments
using three publicly available medical datasets with different imaging
techniques (i.e., Kvasir (endoscopic images), FETAL_PLANES_DB (ultrasound
images) and Covid19-Pneumonia-Normal Chest X-Ray (X-ray images)) and two
private datasets built by ourselves. Experimental results show that the
proposed MedMamba performs well in detecting lesions in various medical images.
To the best of our knowledge, this is the first Vision Mamba tailored for
medical image classification. The purpose of this work is to establish a new
baseline for medical image classification tasks and provide valuable insights
for the future development of more efficient and effective SSM-based artificial
intelligence algorithms and application systems in the medical. Source code has
been available at https://github.com/YubiaoYue/MedMamba.
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