FETAL-GAUGE：评估胎儿超声视觉语言模型的基准

ICLR 2026 Poster accepted paper at ICLR 2026. The growing demand for prenatal ultrasound imaging has intensified a global shortage of trained sonographers, creating barriers to essential fetal health monitoring. Deep learning has the potential to enhance sonographers' efficiency and support the training of new practitioners. Vision-Language Models (VLMs) are particularly promising for ultrasound interpretation, as they can jointly process images and text to perform multiple clinical tasks within a single framework. However, despite the expansion of VLMs, no standardized benchmark exists to evaluate their performance in fetal ultrasound imaging. Code/project link: https://github.com/BioMedIA-MBZUAI/FETAL-GAUGE