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.
Enguang Wang, Juxiang Wu, Shuo Gao et al.· IEEE journal of biomedical a...· 0 citations
Automatic radiology report generation has gained increasing attention for its potential to assist in clinical reporting and reduce the workload of radiologists. Existing 3D radiology report generation methods employ multi-modal foundation model to encode volume-text inputs and produce diagnosis reports, while they ignore the characteristics of 3D volumes including much background regions and suffer from generating hallucinations, especially in medical domain that contains many uncommon professional terms. In this paper, we aim to efficiently adapt the pre-trained foundation model to specific 3D radiology report generation, and present a Prior-drIven Prompting with diagnosis-oriented retrieval-Augmentation (PIPA) framework. In PIPA, we design a Prior-drIven Prompting (PIP) strategy to exploit diagnostic knowledge from input volumes and a Diagnosis-oriented volume-report retrievalaugmentation Generation (DIG) module to explore beneficial knowledge from external database. Specifically, in PIP, to take full advantage of the patient's clinical information, e.g., age and symptoms, and the possible disease information, e.g., brain tumor, edema, we formulate them as the patient and disease priors to mine clinical relevant knowledge. Furthermore, we propose utilizing visual and textual embeddings as queries to retrieve similar external data by devising a diagnosis-oriented retrieval-augmentation scheme for leveraging more report resources as references for LLM to produce accuracy outcomes. With PIP and DIG, PIPA integrates clinical priors and external data to learn effective diagnostic representations for high-quality report generation. We evaluate the framework on both public and in-house 3D medical datasets with corresponding reports, demonstrating its strong performance in generating accurate diagnosis reports. Source codes have been published at https://github.com/CUHK-AIM-Group/PIPA/tree/ main.
Qiushi Yang, Wuyang Li, Xiaoqing Guo et al.· IEEE Transactions on Medical...· 0 citations
