Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging.

Abstract

Annotation-efficient deep learning refers to methods and practices that yield high-performance deep learning models without the use of massive carefully labeled training datasets. This paradigm has recently attracted attention from the medical imaging research community because (1) it is difficult to collect large, representative medical imaging datasets given the diversity of imaging protocols, imaging devices, and patient populations, (2) it is expensive to acquire accurate annotations from medical experts even for moderately sized medical imaging datasets, and (3) it is infeasible to adapt data-hungry deep learning models to detect and diagnose rare diseases whose low prevalence hinders data collection.