INA-Net: An integrated noise-adaptive attention neural network for enhanced medical image segmentation

Abstract

Skin cancer, a growing health concern, poses a significant threat. Automated segmentation of skin lesions is crucial for precise clinical diagnosis and treatment. However, challenges such as hair interference, vascular occlusion, and ambiguous lesion boundaries in dermoscopic images complicate accurate segmentation. Recent research has focused on capturing multiscale-enhanced global information across spatial and channel dimensions to improve the segmentation of ambiguous boundaries. Despite these advancements, performance remains suboptimal in the presence of complex disturbances. To address this, we propose INA-Net, an Integrated Noise-Adaptive Attention Neural Network for enhanced medical image segmentation. INA-Net proposes a lightweight noise-range attention module that integrates local and simulated noise features with global information to improve robustness against complex mixed noises. Additionally, our proposed Edge-aware Spatial Attention (ESA) and Multi-scale Channel Attention (MCA) enhance feature information and boundary perception. The proposed Dynamic Noise Encoding module and Fourier Wavelet Analysis (FW-Parser) further improve robustness by handling high-frequency noise components. INA-Net effectively manages hair interference, vascular occlusion, and ambiguous lesion boundaries, thereby providing critical support for accurate lesion identification. Evaluations on the ISIC2016, ISIC2018, and PH2 datasets demonstrate outstanding performance, with IOU scores of 87.53%, 84.74%, and 85.30%, respectively. These results surpass those of other models with comparable complexity and size.