Multi-scale deformable attention fusion network with global context modeling for chest X-ray lesion segmentation

Chest X-ray segmentation is a critical technique for the screening and diagnosis of common thoracic diseases such as pneumonia and COVID-19, providing essential support for clinical diagnosis and quantitative analysis. However, the inherent challenges of chest X-ray images, including blurry lesion boundaries and irregular contours, pose significant difficulties for accurate segmentation. To address this problem, we propose a Multi-Scale Deformable Attention Fusion Network (MSDAFNet) for lesion segmentation in chest X-rays. MSDAFNet adaptively perceives lesion regions of varying shapes and sizes, thereby enhancing the model's robustness to diverse pathological tissues. Specifically, we design a multi-scale deformable convolution attention module that employs channel grouping and dilated convolutions with different dilation rates to capture multi-scale feature maps without increasing parameters. Learnable offsets are introduced, allowing the convolution kernels to adaptively adjust sampling positions. Furthermore, considering the clustered distribution characteristics of small target lesion regions, a memory module is proposed before the decoder.This module establishes semantic correlations between image regions by introducing learnable key-value pairs and an attention mechanism, thereby supplementing convolutional features with the capability of modeling long-range dependencies.Extensive experiments conducted on two large-scale medical datasets, QaTa-COV19-v1 and QaTa-COV19-v2, demonstrate that MSDAFNet achieves higher accuracy and robustness in anatomical structure segmentation than existing methods, delivering competitive performance.