Daniel M. Lang

Predicting breast cancer treatment response just got a major upgrade, with a new framework that outperforms traditional models by effectively modeling temporal imaging data.

Prediction bias in medical imaging models can be effectively mitigated with a new objective that prevents model collapse during test-time adaptation.

Forget simple averaging: this entropy-adaptive model merging strategy unlocks robust performance in medical imaging by intelligently combining models trained on diverse, private clinical datasets.