Medical foundation models converge less under label supervision
Researchers tested how different medical image encoders converge when trained with various objectives like self-supervision versus clinical labels. The findings show that self-supervision, not clinical labeling or model size, drives representation alignment. This means encoders don't need perfect geometric overlap to be functionally interchangeable for diagnosis.