A data-efficient strategy for building high-performing medical foundation models

ABSTRACT Foundation models are pretrained on massive datasets. However, collecting medical datasets is expensive and time-consuming, and raises privacy concerns. Here we show that synthetic

data generated via conditioning with disease labels can be leveraged for building high-performing medical foundation models. We pretrained a retinal foundation model, first with

approximately one million synthetic retinal images with physiological structures and feature distribution consistent with real counterparts, and then with only 16.7% of the 904,170

real-world colour fundus photography images required in a recently reported retinal foundation model (RETFound). The data-efficient model performed as well or better than RETFound across

nine public datasets and four diagnostic tasks; and for diabetic-retinopathy grading, it used only 40% of the expert-annotated training data used by RETFound. We also support the

generalizability of the data-efficient strategy by building a classifier for the detection of tuberculosis on chest X-ray images. The text-conditioned generation of synthetic data may

enhance the performance and generalization of medical foundation models. Access through your institution Buy or subscribe This is a preview of subscription content, access via your

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institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS A FOUNDATION MODEL FOR GENERALIZABLE DISEASE DETECTION FROM RETINAL IMAGES

Article Open access 13 September 2023 A PORTABLE RETINA FUNDUS PHOTOS DATASET FOR CLINICAL, DEMOGRAPHIC, AND DIABETIC RETINOPATHY PREDICTION Article Open access 22 February 2025

SELF-SUPERVISED RETINAL THICKNESS PREDICTION ENABLES DEEP LEARNING FROM UNLABELLED DATA TO BOOST CLASSIFICATION OF DIABETIC RETINOPATHY Article 09 November 2020 DATA AVAILABILITY The main

data supporting the results in this study are available within the paper and its Supplementary Information. Data for pretraining can be accessed through the following weblinks: AIROGS

(https://airogs.grand-challenge.org/data-and-challenge), Kaggle EyePACS (https://www.kaggle.com/c/diabetic-retinopathy-detection), DDR (https://github.com/nkicsl/DDR-dataset), ODIR-2019

(https://odir2019.grand-challenge.org). Data for fine-tuning can be accessed through the following weblinks: IDRID

(https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid), MESSIDOR-2 (https://www.adcis.net/en/third-party/messidor2), APTOS-2019

(https://www.kaggle.com/competitions/aptos2019-blindness-detection/data), PAPILA (https://figshare.com/articles/dataset/PAPILA/14798004/1), Glaucoma Fundus

(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1YRRAC), ORIGA (https://www.kaggle.com/datasets/arnavjain1/glaucoma-datasets), AREDS

(https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000001.v3.p1), JSIEC (https://zenodo.org/record/3477553), Retina

(https://www.kaggle.com/datasets/jr2ngb/cataractdataset), REFUGE (https://ieee-dataport.org/documents/refuge-retinal-fundus-glaucoma-challenge), RIM-ONE-DL

(https://github.com/miag-ull/rim-one-dl?tab=readme-ov-file), CheXpert (https://stanfordmlgroup.github.io/competitions/chexpert/), Shenzhen Hospital CXR Set

(https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/index.html), TB Chest X-ray database

(https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset). CODE AVAILABILITY The code of RETFound-DE is available at https://github.com/Jonlysun/DERETFound (ref.

64), and an online interactive platform is available at http://fdudml.cn:12001. We used stable diffusion implemented by diffusers (https://github.com/huggingface/diffusers) for the backbone

and image generation. The heat maps were generated with GradCam (https://github.com/jacobgil/pytorch-grad-cam) and the _t_-SNE visualization was generated with tsne-pytorch

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ACKNOWLEDGEMENTS We acknowledge support for this work provided by the National Natural Science Foundation of China (grant numbers U2001209, 62372117 and 62472102) and the Natural Science

Foundation of Shanghai (grant number 21ZR1406600). The computations in this research were performed using the CFFF platform of Fudan University. The AREDS dataset used for the analyses

described in this paper was obtained from the Age-Related Eye Disease Study (AREDS) Database found at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000001.v3.p1

through dbGaP accession number phs000001.v3.p1. Funding support for AREDS was provided by the National Eye Institute (N01-EY-0-2127). We thank the AREDS participants and the AREDS Research

Group for their valuable contribution to this research. AUTHOR INFORMATION Author notes * These authors contributed equally: Yuqi Sun, Weimin Tan. AUTHORS AND AFFILIATIONS * Shanghai Key

Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China Yuqi Sun, Weimin Tan, Zhuoyao Gu, Ruian He, Siyuan Chen, Miao Pang & Bo

Yan Authors * Yuqi Sun View author publications You can also search for this author inPubMed Google Scholar * Weimin Tan View author publications You can also search for this author inPubMed

 Google Scholar * Zhuoyao Gu View author publications You can also search for this author inPubMed Google Scholar * Ruian He View author publications You can also search for this author

inPubMed Google Scholar * Siyuan Chen View author publications You can also search for this author inPubMed Google Scholar * Miao Pang View author publications You can also search for this

author inPubMed Google Scholar * Bo Yan View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS B.Y. and W.T. supervised the research. B.Y.

conceived the technique. Y.S. implemented the algorithm. Y.S. and W.T. designed the validation experiments. Y.S. trained the network and performed the validation experiments. Y.S. and W.T.

analysed the validation results. Z.G. verified the code. R.H. provided technical support on the implementation of the web page. Z.G., M.P. and S.C. collected the public datasets. Y.S., W.T.

and B.Y. wrote the paper. CORRESPONDING AUTHOR Correspondence to Bo Yan. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW

INFORMATION _Nature Biomedical Engineering_ thanks Pearse Keane and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ADDITIONAL INFORMATION

PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 PERFORMANCE (AUPR)

IN DOWNSTREAM TASKS. A, Internal evaluation. We fine-tuned the pretrained models on nine public datasets across four downstream tasks: diabetic retinopathy grading, glaucoma diagnosis,

age-related macular degeneration (AMD) grading and multi diseases classification. Compared to RETFound, RETFound-DE achieves superior performance on six datasets (P < 0.05) and comparable

performance on the other three datasets (P > 0.05). B, External evaluation. Models are fine-tuned on one diabetic retinopathy grading dataset and evaluated on the others. RETFound-DE

outperforms RETFound when fine-tuned on APTOS-2019 and evaluated on IDRID, or when fine-tuned on IDRID and evaluated on MESSIDOR-2. We present the mean value of AUPR on each bar and the

error bars show 95% confidence intervals. P-value was calculated with the two-sided t-test and listed in the figure. EXTENDED DATA FIG. 2 CONFUSION MATRICES COMPARISON IN DOWNSTREAM TASKS.

The confusion matrices shows the comparison between the predicted classes and the actual labels by the models, with each element of the matrix representing the prediction distribution for a

specific class. RETFound-DE’s diagonal prediction scores are generally higher than SSL-ImageNet, and even higher than RETFound, indicating that it classifies each class more accurately in

every task and has a lower misclassification rate. EXTENDED DATA FIG. 3 LABEL AND TRAINING TIME EFFICIENCY (AUPR). A, Label efficiency. Label efficiency refers to the amount of training data

and labels to achieve the target performance for a deep learning networks. RETFound-DE and RETFound show superior label efficiency than SSL-ImageNet. RETFound-DE even outperforms RETFound

on Retina dataset. B, Training time efficiency. Training time efficiency refers to the fine-tuning time to achieve the target performance for foundation models when adapted to downstream

datasets. RETFound-DE enables same performance as RETFound in less training time on several datasets, such as IDRID and Retina. The dashed grey lines highlight the significant difference

between RETFound and RETFound-DE. EXTENDED DATA FIG. 4 PERFORMANCE AND PRETRAINING TIME COMPARISONS OF SSL-IMAGENET-RETINAL AND RETFOUND-DE (AUPR). A, The effect of synthetic data on using

different number of real data for pretraining. The efficacy of SSL-ImageNet-Retinal progressively enhances on four downstream tasks as the quantity of real retinal images used for

pretraining increases. By pretraining on generated images, RETFound-DE shows a significant performance improvement than SSL-ImageNet-Retinal. On IDRID, MESSIDOR-2 and Retina datasets,

RETFound-DE outperforms RETFound when pretrained on only 40k real retinal images. B, The performance of RETFound-DE and SSL-ImageNet-Retinal (150k) over a matched computational time from 5

to 6 8-A100 days. We use 8-A100 day as a unit to denote the pretraining time of using 8 NVIDIA A100 GPUs for one day. For both models, the pretraining dataset at this time period is 150k

real retinal image dataset. RETFound-DE consistently outperforms SSL-ImageNet-Retinal (150k) on all four downstream datasets within the same pretraining time. EXTENDED DATA FIG. 5 INTERNAL

AND EXTERNAL EVALUATION ON CHEST X-RAY IMAGES. A, Internal evaluation. CXRFM and CXRFM-DE perform comparably on the ShenzhenCXR dataset and significantly outperformed SSL-ImageNet. On the

TBChest dataset, all three models achieved similar performance, with an AUROC exceeding 0.99. B, External evaluation. When fine-tuned on TBChest and evaluated on ShenzhenCXR, CXRFM and

CXRFM-DE perform similarly, both substantially surpassing SSL-ImageNet. When fine-tuned on ShenzhenCXR and evaluated on TBChest, CXRFM outperforms CXRFM-DE. C, CXRFM-DE (denoted as ‘With

synthetic data’) significantly outperforms SSL-ImageNet-Chest (20k) (denoted as ‘Without synthetic data’) under both conditions, demonstrating the enhancement in generalization brought about

by synthetic data. EXTENDED DATA FIG. 6 FEATURE DISTRIBUTION OF REAL AND SYNTHETIC DATASETS IN TERMS OF AGE AND GENDER. We use histograms and cumulative distribution functions to illustrate

the feature distributions of real data and synthetic data. (A) and (B) show that the features distribution of real and synthetic data for the 0<Age<60 and Age>60 groups. (C) and

(D) show that the features distribution of real and synthetic data for female and male. The results show the consistency of feature distribution between real and synthetic datasets in terms

of age and gender. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary notes, figures and captions for the supplementary tables. REPORTING SUMMARY SUPPLEMENTARY TABLES

Supplementary Tables 1–9. RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with

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law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Sun, Y., Tan, W., Gu, Z. _et al._ A data-efficient strategy for building high-performing medical foundation models. _Nat.

Biomed. Eng_ 9, 539–551 (2025). https://doi.org/10.1038/s41551-025-01365-0 Download citation * Received: 10 January 2024 * Accepted: 04 February 2025 * Published: 05 March 2025 * Issue Date:

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