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.

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客座社论注释高效的深度学习：医学成像的圣杯

注释高效的深度学习是指在不使用大量仔细标记的训练数据集的情况下产生高性能深度学习模型的方法和实践。这种范式最近引起了医学成像研究界的关注，因为 (1) 鉴于成像协议、成像设备和患者群体的多样性，很难收集大型、具有代表性的医学成像数据集，(2) 获得准确的数据成本很高即使对于中等规模的医学影像数据集，医学专家的注释也是如此，并且 (3) 采用数据密集型深度学习模型来检测和诊断低流行率阻碍数据收集的罕见疾病是不可行的。