FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
Journal:
arXiv
Published Date:
Feb 20, 2025
Abstract
Foundation models are becoming increasingly effective in the medical domain,
offering pre-trained models on large datasets that can be readily adapted for
downstream tasks. Despite progress, fetal ultrasound images remain a
challenging domain for foundation models due to their inherent complexity,
often requiring substantial additional training and facing limitations due to
the scarcity of paired multimodal data. To overcome these challenges, here we
introduce FetalCLIP, a vision-language foundation model capable of generating
universal representation of fetal ultrasound images. FetalCLIP was pre-trained
using a multimodal learning approach on a diverse dataset of 210,035 fetal
ultrasound images paired with text. This represents the largest paired dataset
of its kind used for foundation model development to date. This unique training
approach allows FetalCLIP to effectively learn the intricate anatomical
features present in fetal ultrasound images, resulting in robust
representations that can be used for a variety of downstream applications. In
extensive benchmarking across a range of key fetal ultrasound applications,
including classification, gestational age estimation, congenital heart defect
(CHD) detection, and fetal structure segmentation, FetalCLIP outperformed all
baselines while demonstrating remarkable generalizability and strong
performance even with limited labeled data. We plan to release the FetalCLIP
model publicly for the benefit of the broader scientific community.
