Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature extraction solution for many downstream tasks, as it only uses unlabeled data. Recently, SSL methods based on instance discrimination have gained popularity in the medical imaging domain. However, SSL pre-trained encoders may use many clues in the image to discriminate an instance that are not necessarily disease-related. Moreover, pathological patterns are often subtle and heterogeneous, requiring the ability of the desired method to represent anatomy-specific features that are sensitive to abnormal changes in different body parts. In this work, we present a novel SSL framework, named DrasCLR, for 3D medical imaging to overcome these challenges. We propose two domain-specific contrastive learning strategies: one aims to capture subtle disease patterns inside a local anatomical region, and the other aims to represent severe disease patterns that span larger regions. We formulate the encoder using conditional hyper-parameterized network, in which the parameters are dependant on the anatomical location, to extract anatomically sensitive features. Extensive experiments on large-scale computer tomography (CT) datasets of lung images show that our method improves the performance of many downstream prediction and segmentation tasks. The patient-level representation improves the performance of the patient survival prediction task. We show how our method can detect emphysema subtypes via dense prediction. We demonstrate that fine-tuning the pre-trained model can significantly reduce annotation efforts without sacrificing emphysema detection accuracy. Our ablation study highlights the importance of incorporating anatomical context into the SSL framework.

翻译：带标注的大规模三维医学影像数据稀缺、成本高昂且耗时巨大。自监督学习（SSL）仅需利用无标注数据，为众多下游任务提供了有前景的预训练与特征提取方案。近年来，基于实例判别（instance discrimination）的SSL方法在医学影像领域广受欢迎。然而，SSL预训练编码器可能利用图像中与疾病无关的线索来区分实例。此外，病理模式通常细微且异质性强，要求目标方法具备表征对不同身体部位异常变化敏感的解剖特异性特征的能力。为克服这些挑战，本文提出名为DrasCLR的新型SSL框架，专攻三维医学成像。我们设计了两种领域特定的对比学习策略：其一旨在捕捉局部解剖区域内的细微病变模式，其二旨在表征跨越较大区域的严重病变模式。我们采用条件超参数化网络构建编码器，其参数随解剖位置动态变化，以提取具解剖敏感性的特征。在肺部图像的大规模计算机断层扫描（CT）数据集上进行的广泛实验表明，我们的方法提升了下游多项预测与分割任务的性能。患者级表征改善了患者生存预测任务的效果。通过密集预测，我们展示了方法对肺气肿亚型的检测能力。实验证明，微调预训练模型可在不牺牲肺气肿检测精度的前提下显著减少标注工作量。消融研究强调了将解剖上下文融入SSL框架的重要性。