Biomedical Image Analysis Group, Department of Computing, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
Nat Commun. 2020 Jul 22;11(1):3673. doi: 10.1038/s41467-020-17478-w.
Causal reasoning can shed new light on the major challenges in machine learning for medical imaging: scarcity of high-quality annotated data and mismatch between the development dataset and the target environment. A causal perspective on these issues allows decisions about data collection, annotation, preprocessing, and learning strategies to be made and scrutinized more transparently, while providing a detailed categorisation of potential biases and mitigation techniques. Along with worked clinical examples, we highlight the importance of establishing the causal relationship between images and their annotations, and offer step-by-step recommendations for future studies.
高质量标注数据的稀缺性，以及开发数据集与目标环境之间的不匹配。从因果角度看待这些问题，可以更透明地做出关于数据收集、标注、预处理和学习策略的决策，并对潜在的偏差和缓解技术进行详细分类。我们结合实际的临床案例，强调了在图像与其标注之间建立因果关系的重要性，并为未来的研究提供了逐步的建议。
