arXiv 2307.110172023-07-20cs.CVcs.LGeess.IV

用于可解释心肌梗死预测的多目标点云自编码器

Multi-objective point cloud autoencoders for explainable myocardial infarction prediction

University of Oxford（牛津大学）

Marcel Beetz, Abhirup Banerjee, Vicente Grau

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AI总结：

针对临床常用生物标志物诊断准确性受限的问题，提出多目标点云自编码器，通过低维潜空间连接多任务分支实现重建与心梗预测，在UK Biobank数据集上表现优于基准且具备可解释性。

摘要：

心肌梗死（MI）是全球最常见的死亡原因之一。临床常用的基于影像的生物标志物（如射血分数）无法捕捉心脏3D解剖结构中更复杂的模式，因而限制了诊断准确性。在本研究中，我们提出多目标点云自编码器，作为一种用于可解释梗死预测的新型几何深度学习方法，其基础是心脏解剖结构与功能的多类别3D点云表征。该模型的架构由多个任务特定分支组成，这些分支通过低维潜空间连接，可同时实现重建与MI预测的高效多目标学习，同时在可解释的潜空间中捕捉病理特异性3D形状信息。此外，其采用基于点云的深度学习操作的分层分支设计，能够直接在高分辨率解剖点云上实现高效的多尺度特征学习。我们在大型UK Biobank（英国生物银行）数据集上开展的实验显示，多目标点云自编码器能够准确重建多时序3D形状，预测解剖结构与输入解剖结构之间的Chamfer距离低于底层影像的像素分辨率。在 incident MI（发病型心肌梗死）预测任务中，我们的方法在受试者工作特征曲线下面积指标上优于多个机器学习与深度学习基准模型19%。此外，其任务特定的紧凑潜空间呈现出易于区分的对照组与MI簇，受试者编码与对应3D形状之间存在临床层面合理的关联，由此证明了预测的可解释性。

英文摘要：

Myocardial infarction (MI) is one of the most common causes of death in the world. Image-based biomarkers commonly used in the clinic, such as ejection fraction, fail to capture more complex patterns in the heart's 3D anatomy and thus limit diagnostic accuracy. In this work, we present the multi-objective point cloud autoencoder as a novel geometric deep learning approach for explainable infarction prediction, based on multi-class 3D point cloud representations of cardiac anatomy and function. Its architecture consists of multiple task-specific branches connected by a low-dimensional latent space to allow for effective multi-objective learning of both reconstruction and MI prediction, while capturing pathology-specific 3D shape information in an interpretable latent space. Furthermore, its hierarchical branch design with point cloud-based deep learning operations enables efficient multi-scale feature learning directly on high-resolution anatomy point clouds. In our experiments on a large UK Biobank dataset, the multi-objective point cloud autoencoder is able to accurately reconstruct multi-temporal 3D shapes with Chamfer distances between predicted and input anatomies below the underlying images' pixel resolution. Our method outperforms multiple machine learning and deep learning benchmarks for the task of incident MI prediction by 19% in terms of Area Under the Receiver Operating Characteristic curve. In addition, its task-specific compact latent space exhibits easily separable control and MI clusters with clinically plausible associations between subject encodings and corresponding 3D shapes, thus demonstrating the explainability of the prediction.

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