论文标题
使用图神经网络的上下文感知的医学图像的自我监督学习
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
论文作者
论文摘要
尽管自我监督的学习使我们能够通过利用未标记的数据来引导培训，但自然图像的通用自我监督方法不能充分纳入上下文。对于医学图像，理想的方法应足够敏感，以检测与每个解剖区域的正常表现组织的偏差。在这里，解剖学是背景。我们介绍了一种新颖的方法，具有两个级别的自我监督的表示目标：一个在区域解剖学层面上，另一种是在患者级别上。我们使用图神经网络来结合不同解剖区域之间的关系。图表的结构由每个患者与解剖图谱之间的解剖对应关系告知。此外，图表具有完整分辨率的任何任意大小的图像的优点。大规模计算机断层扫描（CT）肺图像数据集的实验表明，我们的方法与不考虑上下文的基线方法相比有利。我们使用学到的嵌入来分期与Covid-19有关的肺组织异常。
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 learned embedding for staging lung tissue abnormalities related to COVID-19.
