The result is a fair, end‑to‑end comparison that isolates what actually drives performance for radiology foundation models.

#AI #MedicalImaging #FoundationModels #ScalingLaws #Radiology

including not just findings but also lines & tubes classification/segmentation and report generation. We also test the effect of adding structured labels alongside reports during CLIP‑style pretraining, and study scaling laws under these controlled conditions.

That makes it hard to tell whether wins come from the model design or just from more data/compute or favorable benchmarks. We fix this by holding the pretraining dataset and compute constant and standardizing evaluation across tasks,

Why this matters: Prior comparisons of radiology encoders have often been apples‑to‑oranges: models trained on different datasets, with different compute budgets, and evaluated mostly on small datasets of finding‑only tasks.

✅ Pretrained on 3.5M CXRs to study scaling laws for radiology models
✅ Compared MedImageInsight (CLIP-based) vs RAD-DINO (DINOv2-based)
✅ Found that structured labels + text can significantly boost performance
✅ Showed that as little as 30k in-domain samples can outperform public foundation models