RAC-Net: Interpretable Medical Small Target Segmentation Network With X-Ray Radiation Attenuation Characterization

Medical image target segmentation is critical for clinical diagnosis and treatment, yet mainstream methods perform poorly on small medical targets due to inefficient sparse feature extraction and severe background interference. To tackle this issue, we propose Radiation Attenuation Characteristics Network (RAC-Net) inspired by the X-ray attenuation physical prior in CT imaging, aiming to boost small target segmentation accuracy for early lesion detection and localization. Specifically, RAC-Net integrates two tailored innovative modules: the Radiation Attenuation Salient Feature Extraction (RASFE) module leverages X-ray attenuation principles to extract small target Gaussian saliency features and suppress background noise, enhancing sparse feature representation; the Partial Volume Artifact Edge Enhancement (PVA) module embedded with CT Hounsfield Unit value prior fuses learnable neighborhood weights and physical prior weights, alleviating feature distortion and edge blurring caused by partial volume artifacts, while improving model robustness via clinical medical priors. Extensive experiments validate that RAC-Net achieves state-of-the-art performance in medical small target segmentation, with 18% DICE and 19% IOU improvements over advanced existing methods, providing solid support for early disease diagnosis and intervention.