SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection

TL;DR

SP-Mamba is a novel unsupervised medical anomaly detection framework that leverages spatial perception and structural regularity of medical images, outperforming existing CNN and transformer methods in efficiency and accuracy.

Contribution

The paper introduces SP-Mamba, a new spatial-perception Mamba model with window-sliding and Circular-Hilbert scanning techniques for improved anomaly detection in medical images.

Findings

Achieves state-of-the-art performance on three benchmarks.

Demonstrates robustness and efficiency in anomaly detection.

Effectively exploits anatomical regularity and spatial information.

Abstract

Radiography imaging protocols target on specific anatomical regions, resulting in highly consistent images with recurrent structural patterns across patients. Recent advances in medical anomaly detection have demonstrated the effectiveness of CNN- and transformer-based approaches. However, CNNs exhibit limitations in capturing long-range dependencies, while transformers suffer from quadratic computational complexity. In contrast, Mamba-based models, leveraging superior long-range modeling, structural feature extraction, and linear computational efficiency, have emerged as a promising alternative. To capitalize on the inherent structural regularity of medical images, this study introduces SP-Mamba, a spatial-perception Mamba framework for unsupervised medical anomaly detection. The window-sliding prototype learning and Circular-Hilbert scanning-based Mamba are introduced to better exploit consistent anatomical patterns and leverage spatial information for medical anomaly detection. Furthermore, we excavate the concentration and contrast characteristics of anomaly maps for improving anomaly detection. Extensive experiments on three diverse medical anomaly detection benchmarks confirm the proposed method's state-of-the-art performance, validating its efficacy and robustness. The code is available at https://github.com/Ray-RuiPan/SP-Mamba.