Annotation-efficient deep learning for automatic medical image segmentation [article]

Shanshan Wang, Cheng Li, Rongpin Wang, Zaiyi Liu, Meiyun Wang, Hongna Tan, Yaping Wu, Xinfeng Liu, Hui Sun, Rui Yang, Xin Liu, Jie Chen (+3 others)
<span title="2021-09-23">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... hat 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2012.04885v3</a> <a target="_blank" rel="external noopener" href="">fatcat:hsmypf4ixzgyrbm4nvxf5e6rye</a> </span>
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