Navya Vangala

A model trained solely on file metadata—with zero medical imaging data—hits 0.992 balanced accuracy on a flagship pediatric x-ray benchmark, proving that evaluation artifacts and pipeline choices dominate architectural gains.

Cross-lingual data pooling acts as a powerful low-resource jumpstart—boosting macro-F1 by up to 0.43 at 25 labels—but predictably degenerates into negative transfer once local annotations surpass 800 examples.