标题： 从潜在图表示中生成保持结构的医学图像 显示英文标题

标题： Structure-Preserving Medical Image Generation from a Latent Graph Representation

摘要： 监督学习技术在许多数据丰富的应用中已证明了其有效性。 然而，由于医学影像数据的获取成本高且数据特征复杂，将这些方法应用于医学影像具有挑战性，从而限制了深度神经网络的全部潜力。 为了解决数据不足的问题，增强技术利用几何、颜色和生成模型（GMs）的合成能力。 尽管之前已有努力，但生成过程中的缺陷限制了数据增强对提高医学影像理解的影响，例如，某些领域（如X射线图像）的高度结构化特性被忽视。 当前的GMs仅依赖网络的能力来盲目合成保留胸部X射线图像语义关系的增强数据，如解剖限制、代表性结构或跨数据集的结构相似性。 在本文中，我们引入了一种新的GM，该GM通过学习潜在图表示（LGR）来利用医学图像的结构相似性。 我们设计了一个端到端模型来学习（i）一个捕捉X射线图像固有结构的LGR，以及（ii）一个从LGR重建X射线图像的图卷积网络（GCN）。 我们采用对抗训练来指导生成器和判别器模型学习所学LGR的分布。 使用所学的GCN，我们的方法通过将生成的LGR映射到X射线来生成保持结构的合成图像。 此外，我们评估了所学的图表示在其他任务中的性能，如X射线图像分类和分割。 数值实验表明了我们方法的有效性，分别使分类和分割的性能提高了$3\%$和$2\%$。 显示英文摘要

摘要： Supervised learning techniques have proven their efficacy in many applications with abundant data. However, applying these methods to medical imaging is challenging due to the scarcity of data, given the high acquisition costs and intricate data characteristics of those images, thereby limiting the full potential of deep neural networks. To address the lack of data, augmentation techniques leverage geometry, color, and the synthesis ability of generative models (GMs). Despite previous efforts, gaps in the generation process limit the impact of data augmentation to improve understanding of medical images, e.g., the highly structured nature of some domains, such as X-ray images, is ignored. Current GMs rely solely on the network's capacity to blindly synthesize augmentations that preserve semantic relationships of chest X-ray images, such as anatomical restrictions, representative structures, or structural similarities consistent across datasets. In this paper, we introduce a novel GM that leverages the structural resemblance of medical images by learning a latent graph representation (LGR). We design an end-to-end model to learn (i) a LGR that captures the intrinsic structure of X-ray images and (ii) a graph convolutional network (GCN) that reconstructs the X-ray image from the LGR. We employ adversarial training to guide the generator and discriminator models in learning the distribution of the learned LGR. Using the learned GCN, our approach generates structure-preserving synthetic images by mapping generated LGRs to X-ray. Additionally, we evaluate the learned graph representation for other tasks, such as X-ray image classification and segmentation. Numerical experiments demonstrate the efficacy of our approach, increasing performance up to $3\%$ and $2\%$ for classification and segmentation, respectively.