Annotation-efficient deep learning for automatic medical image segmentation
arXiv:2012.04885 · doi:10.1038/s41467-021-26216-9
Abstract
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.
References in corpus (8)
- Distilling the Knowledge in a Neural Network
- CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
- A Closer Look at Memorization in Deep Networks
- Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
- Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis
- How does Disagreement Help Generalization against Label Corruption?
- Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
- Data Distillation: Towards Omni-Supervised Learning