Augmented Equivariant Mesh Networks for Anatomical Mesh Segmentation (ICML 2026 Workshops) [R]

Paper: https://arxiv.org/abs/2605.08172 Workshops: AI for Science & Structured Data for Health at ICML 2026 Abstract: Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and mesh resolution variation. Existing task-specific mesh and point-cloud methods are not equivariant, and can degrade sharply under test-time perturbation, for example dropping by 25-26 IoU points on intraoral scan segmentation at 40 o tilt. We present EAMS, an Equivariant Anatomical Mesh Segmentor built on Equivariant Mesh Neural Networks (EMNN), and evaluate it across four clinically distinct tasks spanning edge-, vertex-, and face-level supervision. We combine intrinsic mesh descriptors with anatomy-aware priors, including PCA-derived frames for dental arches and liver surfaces, and augment message passing to provide lightweight global context. Across intracranial aneurysm and intraoral segmentation, EAMS variants are competitive with specialized baselines on unperturbed inputs while remaining stable under geometric perturbations, and on liver surfaces they expose a favorable trade-off between canonical-pose accuracy and rotation robustness. These results show that a lightweight (<2M parameters) equivariant framework can deliver robust anatomical mesh segmentation across diverse supervision types without task-specific architectures. Hi everyone I’m excited to share my solo paper "Augmented Equivariant Mesh Networks for Anatomical Mesh Segmentation" which has been accepted for poster presentations at the ICML 2026 workshops on AI for Science and Structured Data for Health . The project stemmed from my parallel research on structural encoders for biomolecules where enforcing roto-translational equivariance is standard. In this work, I wanted to extend those principles directly to various 3D medical meshes. While current anatomical mesh segmentation methods are highly disjoint and anatomy-specific, we pre