A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling
Foundation models for medical imaging are typically pretrained on increasingly large datasets, following a "scale-at-all-costs" paradigm. However, this strategy faces two critical challenges: large-scale medical datasets often contain substantial redundancy and severe class imbalance that bias representation learning toward over-represented patterns, and indiscriminate training regardless of heterogeneity in data quality incurs considerable computational inefficiency. Here we demonstrate that active, principled data curation during pretraining can serve as a viable, cost-effective alternative to brute-force dataset enlargement. We introduce CheXficient, a chest X-ray (CXR) foundation mode...
| Authors | Chong Wang, Yabin Zhang, Yunhe Gao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 February 2026 |
| Last updated at source | 26 February 2026 |
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