Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
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DOI：
10.1609/aaai.v35i6.16620
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Li Sun;Ke Yu;K. Batmanghelich
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文献类型：
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作者：
Li Sun;Ke Yu;K. Batmanghelich
监督学习方法需要大量的注释数据集。收集这些数据集既耗时又昂贵。到目前为止，很少有带注释的COVID-19成像数据集可用。虽然自监督学习使我们能够通过利用未标记的数据来引导训练，但用于自然图像的通用自监督方法并没有充分结合上下文。对于医学图像，期望的方法应该足够灵敏以检测与每个解剖区域的正常外观组织的偏差;这里，解剖结构是背景。我们介绍了一种新的方法，具有两个层次的自我监督表示学习目标：一个在区域解剖水平上，另一个在患者水平上。我们使用图形神经网络来整合不同解剖区域之间的关系。图的结构由每个患者与解剖图谱之间的解剖对应关系来告知。此外，图形表示具有以全分辨率处理任何任意大小的图像的优点。大规模计算机断层扫描（CT）数据集上的肺图像的实验表明，我们的方法相比，不考虑上下文的基线方法。我们使用学习的嵌入来量化COVID-19的临床进展，并表明我们的方法可以很好地推广到来自不同医院的COVID-19患者。定性结果表明，我们的模型可以识别图像中的临床相关区域。
Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging datasets are available. Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently incorporate the context. For medical images, a desirable method should be sensitive enough to detect deviation from normal-appearing tissue of each anatomical region; here, anatomy is the context. We introduce a novel approach with two levels of self-supervised representation learning objectives: one on the regional anatomical level and another on the patient-level. We use graph neural networks to incorporate the relationship between different anatomical regions. The structure of the graph is informed by anatomical correspondences between each patient and an anatomical atlas. In addition, the graph representation has the advantage of handling any arbitrarily sized image in full resolution. Experiments on large-scale Computer Tomography (CT) datasets of lung images show that our approach compares favorably to baseline methods that do not account for the context. We use the learnt embedding to quantify the clinical progression of COVID-19 and show that our method generalizes well to COVID-19 patients from different hospitals. Qualitative results suggest that our model can identify clinically relevant regions in the images.