论文标题
通过基于里程碑的模型，具有异质标签的多中心解剖分割
Multi-center anatomical segmentation with heterogeneous labels via landmark-based models
论文作者
论文摘要
从多中心数据集中的异质标签中学习解剖学细分是在临床场景中遇到的一种常见情况，在这些情况下，仅在来自特定医疗中心的图像中注释了某些解剖结构，但在整个数据库中却没有。在这里，我们首先展示了最新的像素级细分模型在由于域记忆问题和标签冲突而导致的这项任务时如何失败。然后，我们建议采用Hybridgnet，这是一种基于具有里程碑意义的分割模型，该模型使用基于图表的表示来学习可用的解剖结构。通过分析这两种模型学到的潜在空间，我们表明Hybridgnet自然会学习更多的域不变特征表示，并在胸部X射线多类分段的背景下提供经验证据。我们希望这些见解能阐明使用公共和多中心数据集的异质标签对深度学习模型的培训。
Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are only annotated in images coming from particular medical centers, but not in the full database. Here we first show how state-of-the-art pixel-level segmentation models fail in naively learning this task due to domain memorization issues and conflicting labels. We then propose to adopt HybridGNet, a landmark-based segmentation model which learns the available anatomical structures using graph-based representations. By analyzing the latent space learned by both models, we show that HybridGNet naturally learns more domain-invariant feature representations, and provide empirical evidence in the context of chest X-ray multiclass segmentation. We hope these insights will shed light on the training of deep learning models with heterogeneous labels from public and multi-center datasets.
