Extended Data Fig. 4: Performance and pretraining time comparisons of SSL-ImageNet-Retinal and RETFound-DE (AUPR).
From: A data-efficient strategy for building high-performing medical foundation models
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.
