[论文解读] Robust and Efficient Medical Imaging with Self-Supervision

Shekoofeh Azizi, Laura Culp+32 more|arXiv (Cornell University)|May 19, 2022

Radiomics and Machine Learning in Medical Imaging被引用 31

一句话总结

REMEDIS 是一种统一的表示学习策略，将大规模有监督迁移学习与自监督学习相结合，以在医疗影像人工智能中提高鲁棒性和数据效率，实现对分布内强劲增益和数据高效泛化。

ABSTRACT

Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optimal "out-of-distribution" performance when evaluated in clinical settings different from the training environment. A common mitigation strategy is to develop separate systems for each clinical setting using site-specific data [1]. However, this quickly becomes impractical as medical data is time-consuming to acquire and expensive to annotate [2]. Thus, the problem of "data-efficient generalization" presents an ongoing difficulty for Medical AI development. Although progress in representation learning shows promise, their benefits have not been rigorously studied, specifically for out-of-distribution settings. To meet these challenges, we present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. REMEDIS uses a generic combination of large-scale supervised transfer learning with self-supervised learning and requires little task-specific customization. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data. REMEDIS exhibits significantly improved in-distribution performance with up to 11.5% relative improvement in diagnostic accuracy over a strong supervised baseline. More importantly, our strategy leads to strong data-efficient generalization of medical imaging AI, matching strong supervised baselines using between 1% to 33% of retraining data across tasks. These results suggest that REMEDIS can significantly accelerate the life-cycle of medical imaging AI development thereby presenting an important step forward for medical imaging AI to deliver broad impact.

研究动机与目标

• 在多样的临床环境中激发对数据高效泛化的需求在医学影像 AI 中的重要性。
• 提出 REMEDIS 作为一个统一的表示学习框架，以增强鲁棒性和数据效率。
• 在多样的医学影像任务和现实的回顾性场景中评估 REMEDIS。
• 量化在分布内性能和少样本数据场景中相对于有监督基线的改进。

提出的方法

• 在一个统一框架中将大规模有监督迁移学习与自监督学习结合。
• 利用自监督目标在无需大量任务特定标签的情况下学习表示。
• 表明 REMEDIS 在提升鲁棒性的同时对任务特定自定义的需求很小。
• 在多种医疗影像任务上进行评估，并用回顾性数据模拟三个现实的应用场景。
• 展示诊断准确性提高与数据高效泛化的双重提升。

实验结果

研究问题

• RQ1 统一的表示学习策略是否能够提高对医学影像分布偏移的鲁棒性？
• RQ2 将有监督迁移学习与自监督学习结合如何影响医学影像任务的数据效率？
• RQ3 在使用有限的再训练数据的情况下，REMEDIS 在多大程度上可以达到强有监督基线？
• RQ4 在现实情景下，REMEDIS 是否能够在多样的医学影像模态和任务中实现泛化？

主要发现

• REMEDIS 在分布内对一个强有监督基线的诊断准确率实现高达 11.5% 的相对提升。
• 该方法实现了强大的数据高效泛化，在各任务中仅使用 1% 到 33% 的再训练数据就能达到与强有监督基线相匹配的水平。
• REMEDIS 在多样的医学影像任务和模拟临床情境中展示了鲁棒性与高效性。
• 该框架对任务特定自定义要求很少，有助于在医学影像 AI 管线中更广泛、也更快地部署。