SM-FSL: Similarity Guided Multi-Source Few-Shot Learning for Lung Ultrasound Diagnosis

Lung ultrasound (LUS), valued for its portability and AI-driven analysis, has become an essential di agnostic tool in emergency medicine, critical care, and the screening of infectious diseases. However, the scarcity of annotated data and limited expert availability remain major barriers to large-scale deployment. In this study, we pro pose a Similarity-Guided Multi-Source Few-Shot Learning (SM-FSL) paradigm that shifts the focus from domain alignment to domain selection, enabling robust feature learning from multiple semantically relevant domains under limited data conditions. Based on this paradigm, we develop the Global-Local Multi-Source Domain Network (GLMD-Net) for LUS analysis in data-scarce scenarios, which first employs the proposed Average Nearest Neighbor Set Distance (ANNSD) to prioritize relevant source domains for pre-training. A similarity-guided optimization mechanism then harmonizes multi-source gradient updates, while a local feature enhancement module and a self-supervised auxiliary task improve robustness against noise and artifacts. Finally, a few-shot adaptation strategy fine-tunes only the classification head for efficient and stable knowledge transfer. Extensive experiments on two public COVID-19 ultrasound datasets demonstrate that our method surpasses state-of-the-art approaches, validating its effectiveness in enhancing generalization and robustness under few-shot settings.