用AI自动评估胎儿超声图像质量，助力资源匮乏地区产前检查。
Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
- 基于预训练视觉语言模型FetalCLIP，用低秩适配实现高效微调。
- 在ACOUSLIC-AI数据集上达到0.757的最高F1分数，优化后达0.771。
- 适合医疗资源有限地区推广，代码开源便于复现与应用。
准确的胎儿生物测量（如腹围）对产前保健至关重要。然而，高质量超声图像的获取高度依赖超声医师的专业水平，这在低收入国家因专业人员稀缺而成为重大挑战。为此，我们利用FetalCLIP——一个在超过21万对胎儿超声图像与对应描述组成的精选数据集上预训练的视觉-语言模型——对盲扫超声数据进行自动化胎儿超声图像质量评估（IQA）。我们提出FetalCLIP$_{CLS}$，一种基于低秩适配（LoRA）从FetalCLIP改进的IQA模型，并在ACOUSLIC-AI数据集上与六个CNN和Transformer基线模型对比。FetalCLIP$_{CLS}$取得0.757的最高F1分数；进一步地，将适配后的分割模型转用于分类任务，性能提升至0.771。本研究证明，通过参数高效的微调，可使胎儿超声基础模型适应特定任务，从而推动资源受限环境下的产前护理发展。实验代码已公开：https://github.com/donglihe-hub/FetalCLIP-IQA。
原文摘要 · Abstract (English)
Accurate fetal biometric measurements, such as abdominal circumference, play a vital role in prenatal care. However, obtaining high-quality ultrasound images for these measurements heavily depends on the expertise of sonographers, posing a significant challenge in low-income countries due to the scarcity of trained personnel. To address this issue, we leverage FetalCLIP, a vision-language model pretrained on a curated dataset of over 210,000 fetal ultrasound image-caption pairs, to perform automated fetal ultrasound image quality assessment (IQA) on blind-sweep ultrasound data. We introduce FetalCLIP$_{CLS}$, an IQA model adapted from FetalCLIP using Low-Rank Adaptation (LoRA), and evaluate it on the ACOUSLIC-AI dataset against six CNN and Transformer baselines. FetalCLIP$_{CLS}$ achieves the highest F1 score of 0.757. Moreover, we show that an adapted segmentation model, when repurposed for classification, further improves performance, achieving an F1 score of 0.771. Our work demonstrates how parameter-efficient fine-tuning of fetal ultrasound foundation models can enable task-specific adaptations, advancing prenatal care in resource-limited settings. The experimental code is available at: https://github.com/donglihe-hub/FetalCLIP-IQA.
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