ZSG-Net: A Zero-Shot Super-Resolution Guided Network for Ultrasound Image Segmentation and Classification

Automated ultrasound (US) image analysis is hindered by challenges stemming from low resolution, noise, and non-uniform grayscale distribution, which compromise image quality. While many existing studies address these issues using super-resolution (SR) techniques, they often focus exclusively on SR without considering downstream tasks or tailoring to the unique characteristics of US images. In this work, we propose ZSG-Net, a zero-shot super-resolution-guided network, designed to bridge the gap between US image quality enhancement and its benefits in segmentation and classification. First, we introduce a zero-shot self-supervised cycle generative adversarial network (ZSCycle-GAN), tailored to the unique characteristics of US images, to perform SR while preserving critical structural details. Unlike conventional SR methods that focus solely on image enhancement, ZSCycle-GAN is designed to optimize downstream tasks. Second, we adopt a zero-shot self-supervised learning strategy, eliminating the reliance on labeled data and addressing the scarcity of annotated medical imaging datasets. Third, we incorporate a random image degradation (RID) strategy to expand the degradation space for clinical US images, enabling robust learning of diverse quality variations. Extensive experiments on three US image datasets validate the effectiveness of the proposed model. Results demonstrate superior performance in segmentation and classification tasks compared to existing approaches, underscoring the potential of our method to improve US image analysis in clinical settings.