NeuroAPS-Net: Neuro-Anatomically Aware Point Cloud Representation for Efficient Alzheimer's Disease Classification

TL;DR

NeuroAPS-Net introduces a neuroanatomically informed point cloud approach for efficient Alzheimer's disease classification, reducing computational costs while maintaining accuracy.

Contribution

The paper presents a novel pipeline converting MRI into anatomically labeled point clouds and a lightweight deep learning model leveraging anatomical priors.

Findings

NeuroAPS-Net achieves competitive accuracy on ADNI-2DPC dataset.

The method significantly reduces inference latency and GPU memory usage.

Anatomically guided point cloud learning is a viable alternative to voxel-based CNNs.

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a major cause of dementia. Structural MRI is widely used to analyze AD-related brain atrophy; however, most deep learning methods rely on computationally expensive 3D convolutional neural networks (CNNs), limiting deployment in resource-constrained settings. This work introduces two main contributions. First, we propose a pipeline that converts T1-weighted MRI into anatomically informed 2D point clouds using Anatomical Priority Sampling (APS), producing ADNI-2DPC, the first neuroanatomically labeled MRI-derived point cloud dataset. Second, we present NeuroAPS-Net, a lightweight geometric deep learning model that incorporates anatomical priors via region-aware feature encoding and ROI token aggregation. Experiments on ADNI-2DPC demonstrate that NeuroAPS-Net achieves competitive classification accuracy while significantly reducing inference latency and GPU memory compared to state-of-the-art point cloud methods. These results highlight the potential of anatomically guided point cloud learning as an efficient and interpretable alternative to voxel-based CNNs for AD classification.