Geometry-Aware Cardiac MRI Representation Learning with Equivariant Neural Fields

Jesse Wiers, David R Wessels, Lukas Arts, Samuel Ruiperez-Campillo, Maarten Kolk, Fleur Tjong, Erik J Bekkers

Keywords: Geometric Deep Learning, Equivariant Neural Fields, Cardiac Risk Stratification

Abstract:

Cardiac MRI encodes detailed geometric information, but standard deep learning models
rely on grid-based encoders that emphasize texture rather than structure. Neural fields of-
fer a continuous alternative, yet Conditional Neural Fields (CNFs) compress each subject
into a single global latent, discarding spatial organization. We evaluate Equivariant Neural
Fields (ENFs) for cardiac MRI, which replace the global latent with a geometry-aware la-
tent point cloud. ENFs achieve competitive reconstruction quality with far fewer decoder
parameters and produce latents that are local, anatomically meaningful, and robust to
geometric transformations. For downstream prediction tasks, ENF latents perform com-
petitively with ResNet50 and global CNF latents across several clinical endpoints. These
results position ENFs as a compact, interpretable, and geometry-aware alternative for car-
diac MRI representation learning

Primary Subject Area: Geometric Deep Learning

Secondary Subject Area: Application: Cardiology

Registration Requirement: Yes

Reproducibility: https://github.com/JesseWiers/enfs_for_cardiac

Visa & Travel: Yes

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 339