# FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis

arXiv: https://arxiv.org/abs/2502.14807 · HTML: https://mlsanity.com/paper/2502.14807/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 70 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 24 of 24)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [GLaMM: Pixel Grounding Large Multimodal Model](https://mlsanity.com/paper/2311.03356/) (2023)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation](https://mlsanity.com/paper/2402.15852/) (2024)
- [Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography](https://mlsanity.com/paper/2403.17834/) (2024)
- [A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision](https://mlsanity.com/paper/2308.07898/) (2023)
- [Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision](https://mlsanity.com/paper/2310.18689/) (2023)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos](https://mlsanity.com/paper/2411.04923/) (2024)
- [ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders](https://mlsanity.com/paper/2308.01317/) (2023)
- [UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities](https://mlsanity.com/paper/2412.10372/) (2024)
- [EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation](https://mlsanity.com/paper/2410.09704/) (2024)
- [EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis](https://mlsanity.com/paper/2409.06644/) (2024)
- [EchoApex: A General-Purpose Vision Foundation Model for Echocardiography](https://mlsanity.com/paper/2410.11092/) (2024)
- [MedFILIP: Medical Fine-grained Language-Image Pre-training](https://mlsanity.com/paper/2501.10775/) (2025)
- [FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning](https://mlsanity.com/paper/2310.12600/) (2023)

## Built on by (most cited first, 24 of 24)

- [EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence](https://mlsanity.com/paper/2509.14977/) (2025)
- [Ultrasound-CLIP: Semantic-Aware Contrastive Pre-training for Ultrasound Image-Text Understanding](https://mlsanity.com/paper/2604.01749/) (2026)
- [A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry](https://mlsanity.com/paper/2512.16710/) (2025)
- [Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings](https://mlsanity.com/paper/2507.22802/) (2025)
- [TPA: Temporal Prompt Alignment for Fetal Congenital Heart Defect Classification](https://mlsanity.com/paper/2508.15298/) (2025)
- [Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation](https://mlsanity.com/paper/2510.12953/) (2025)
- [MedVQA-TREE: A Multimodal Reasoning and Retrieval Framework for Sarcopenia Prediction](https://mlsanity.com/paper/2508.19319/) (2025)
- [Generative deep learning for foundational video translation in ultrasound](https://mlsanity.com/paper/2511.03255/) (2025)
- [FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound](https://mlsanity.com/paper/2512.22278/) (2025)
- [DARK: Diagonal-Anchored Repulsive Knowledge Distillation for Vision-Language Models under Extreme Compression](https://mlsanity.com/paper/2603.05421/) (2026)
- [FetalAgents: A Multi-Agent System for Fetal Ultrasound Image and Video Analysis](https://mlsanity.com/paper/2603.09733/) (2026)
- [ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding](https://mlsanity.com/paper/2604.10916/) (2026)
- [Benchmarking Ultrasound Foundation Models for Fetal Plane Classification](https://mlsanity.com/paper/2605.27796/) (2026)
- [FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model](https://mlsanity.com/paper/2606.11106/) (2026)
- [FetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection](https://mlsanity.com/paper/2606.22487/) (2026)
- [From Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction](https://mlsanity.com/paper/2606.23005/) (2026)
- [SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis](https://mlsanity.com/paper/2606.29586/) (2026)
- [Towards Real-World Ultrasound Understanding: Large Vision-Language Models from Multi-Image Examinations with Long-Form Reports](https://mlsanity.com/paper/2607.01908/) (2026)
- [A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings](https://mlsanity.com/paper/2608.12745/) (2026)
- [Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening](https://mlsanity.com/paper/2608.14763/) (2026)
- [AWR-Net: Decoupling Anatomy and Appearance for 3D Fetal Brain Ultrasound Synthesis](https://mlsanity.com/paper/2609.22635/) (2026)
- [Towards Whole-Study Screening for Congenital Heart Disease in Fetal Ultrasound Using Multiple Instance Learning](https://mlsanity.com/paper/2609.31376/) (2026)
- [WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learning](https://mlsanity.com/paper/2610.04601/) (2026)
- [EarthAlign: Learning Language-Aligned Representations of Earth System Data](https://mlsanity.com/paper/2610.14624/) (2026)

## Abstract (arXiv)

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.