SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection

• URL: http://arxiv.org/abs/2509.01080v1
• Date: Mon, 01 Sep 2025 02:56:45 GMT
• Title: SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection
• Authors: Yao Wang, Dong Yang, Zhi Qiao, Wenjian Huang, Liuzhi Yang, Zhen Qian,
• Abstract summary: We present SpectMamba, the first Mamba-based architecture designed for medical image detection.<n>A key component of SpectMamba is the Hybrid Spatial-Frequency Attention (HSFA) block, which separately learns high- and low-frequency features.<n>We show that SpectMamba achieves state-of-the-art performance while being both effective and efficient across various medical image detection tasks.
• Score: 11.43227481199105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Abnormality detection in medical imaging is a critical task requiring both high efficiency and accuracy to support effective diagnosis. While convolutional neural networks (CNNs) and Transformer-based models are widely used, both face intrinsic challenges: CNNs have limited receptive fields, restricting their ability to capture broad contextual information, and Transformers encounter prohibitive computational costs when processing high-resolution medical images. Mamba, a recent innovation in natural language processing, has gained attention for its ability to process long sequences with linear complexity, offering a promising alternative. Building on this foundation, we present SpectMamba, the first Mamba-based architecture designed for medical image detection. A key component of SpectMamba is the Hybrid Spatial-Frequency Attention (HSFA) block, which separately learns high- and low-frequency features. This approach effectively mitigates the loss of high-frequency information caused by frequency bias and correlates frequency-domain features with spatial features, thereby enhancing the model's ability to capture global context. To further improve long-range dependencies, we propose the Visual State-Space Module (VSSM) and introduce a novel Hilbert Curve Scanning technique to strengthen spatial correlations and local dependencies, further optimizing the Mamba framework. Comprehensive experiments show that SpectMamba achieves state-of-the-art performance while being both effective and efficient across various medical image detection tasks.

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