TinyUSFM: Towards Compact and Efficient Ultrasound Foundation Models

Foundation models for medical imaging demonstrate superior generalization capabilities across diverse anatomical structures and clinical applications. Their outstanding performance relies on substantial computational resources, limiting deployment in resource-constrained clinical environments. This paper presents TinyUSFM, the first lightweight ultrasound foundation model that maintains superior organ versatility and task adaptability of our large-scale Ultrasound Foundation Model (USFM) through knowledge distillation with strategically curated small datasets, delivering significant computational efficiency without sacrificing performance. Considering the limited capacity and representation ability of lightweight models, we propose a feature-gradient driven coreset selection strategy to curate high-quality compact training data, avoiding training degradation from low-quality redundant images. To preserve the essential spatial and frequency domain characteristics during knowledge transfer, we develop domain-separated masked image modeling assisted consistency-driven dynamic distillation. This novel framework adaptively transfers knowledge from large foundation models by leveraging teacher model consistency across different domain masks, specifically tailored for ultrasound interpretation. For evaluation, we establish the UniUS-Bench, the largest publicly available ultrasound benchmark comprising 8 classification and 10 segmentation datasets across 15 organs. Using only 200K images in distillation, TinyUSFM matches USFM's performance with just 6.36% of parameters and 6.40% of GFLOPs. TinyUSFM significantly outperforms the vanilla model by 9.45% in classification and 7.72% in segmentation, surpassing all state-of-the-art lightweight models, and achieving 84.91% average classification accuracy and 85.78% average segmentation Dice score across diverse medical devices and centers.

Diff-CXR: Report-to-CXR generation through a disease-knowledge enhanced diffusion model

Text-To-Image (TTI) generation is significant for controlled and diverse image generation with broad potential applications. Although current medical TTI methods have made some progress in report-to-Chest-Xray (CXR) generation, their generation performance may be limited due to the intrinsic characteristics of medical data. In this paper, we propose a novel disease-knowledge enhanced Diffusion-based TTI learning framework, named Diff-CXR, for medical report-to-CXR generation. First, to minimize the negative impacts of noisy data on generation, we devise a Latent Noise Filtering Strategy that gradually learns the general patterns of anomalies and removes them in the latent space. Then, an Adaptive Vision-Aware Textual Learning Strategy is designed to learn concise and important report embeddings in a domain-specific Vision-Language Model, providing textual guidance for Chest-Xray generation. Finally, by incorporating the general disease knowledge into the pretrained TTI model via a delicate control adapter, a disease-knowledge enhanced diffusion model is introduced to achieve realistic and precise report-to-CXR generation. Experimentally, our Diff-CXR outperforms previous SOTA medical TTI methods by 33.4\% / 8.0\% and 23.8\% / 56.4\% in the FID and mAUC score on MIMIC-CXR and IU-Xray, with the lowest computational complexity at 29.641 GFLOPs. Downstream experiments on three thorax disease classification benchmarks and one CXR-report generation benchmark demonstrate that Diff-CXR is effective in improving classical CXR analysis methods. Notably, models trained on the combination of 1\% real data and synthetic data can achieve a competitive mAUC score compared to models trained on all data, presenting promising clinical applications.