论文标题
自动医学图像细分的注释有效的深度学习
Annotation-efficient deep learning for automatic medical image segmentation
论文作者
论文摘要
自动医疗图像细分在科学研究和医疗保健中起着至关重要的作用。现有的高性能深度学习方法通常依赖具有高质量手动注释的大型培训数据集，这些数据集在许多临床应用中很难获得。在这里，我们介绍了注释有效的深度学习（AIDE），这是一个开源框架，可处理不完美的培训数据集。进行了方法论分析和经验评估，我们证明了助手通过在具有稀缺或嘈杂注释的开放数据集上提供更好的性能，从而超过了传统的全面监督模型。我们进一步在现实生活中的案例研究中测试了乳腺肿瘤分割的助手。三个包含来自三个医疗中心的乳房图像的数据集被采用，并利用10％的训练注释，始终产生与完全监督的对应物或独立放射线医师提供的分割图相当的分割图。使用专家标签的10倍提高效率有可能促进广泛的生物医学应用。
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.
