Unsupervised learning can remove batch effects from medical images, letting models generalize across hospitals without retraining.
Medical image analysis struggles when microscope slides are stained or scanned differently across hospitals—models trained on one site fail at another. This paper introduces a technique that learns to remove these visual differences automatically, making AI models work reliably across different clinical sites without needing labeled examples.