  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.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing