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:具有注解的大型体积医学图像很少见,成本昂贵,时间也难以获取。自我监督的学习(SSL)为许多下游任务提供了一个充满希望的训练前前和特征提取解决方案,因为它只使用未贴标签的数据。最近,基于实例歧视的SSL方法在医学成像领域越来越受欢迎。然而,SSL预先训练的编码器可能使用图像中的许多线索来歧视一个不一定与疾病有关的案例。此外,病理学模式往往微妙且不易分化,需要以理想方法来代表对不同身体部分异常变化敏感的解剖特定特征。在这个工作中,我们为3D医学成像工作提出了一个名为DrasCLR的新型SSL框架,以克服这些挑战。我们提出了两种针对特定域的对比学习战略:一个目的是捕捉本地解剖区域内微妙的疾病模式,另一个目的是代表大区域范围内的重病前模式。我们使用有条件的直位直位直位直位直位解网络来构建解码器,其中的参数取决于解析的直径直径精确定位位置,而无需解剖析的直径直径定位定位位置位置位置位置位置定位,我们用于测测测测测测测测测测的图像的图像的图段段。