AGA3DNet: Anatomy-Guided Gaussian Priors with Multi-view xLSTM for 3D Brain MRI Subtype Classification

TL;DR

This paper introduces AGA3DNet, a novel framework that combines anatomical priors from radiology reports with 3D CNN and xLSTM for improved 3D brain MRI subtype classification, enhancing interpretability and performance.

Contribution

The paper presents a new anatomy-guided approach that integrates report-derived anatomical priors into deep learning models for brain MRI classification, without dense voxel annotations.

Findings

Achieved improved classification performance over baseline models.

Enabled interpretable localization using anatomy-grounded priors.

Demonstrated effectiveness on a retrospective brain MRI cohort.

Abstract

Accurate 3D brain MRI subtype classification benefits from both localized anatomical cues and long-range contextual reasoning. We present AGA3DNet, a report-grounded framework that incorporates brief anatomical phrases extracted from radiology reports as a soft anatomical prior channel and fuses it with a lightweight 3D CNN and multi-view xLSTM aggregation. Specifically, extracted anatomical phrases are mapped to atlas-defined regions and converted into smooth spatial priors using a signed-distance transform followed by Gaussian weighting, providing interpretable, anatomy-grounded guidance without requiring dense voxel annotations. We evaluate AGA3DNet on a retrospective institutional brain MRI cohort for abnormal subtype discrimination and compare against reproducible 3D classification baselines. AGA3DNet achieves improved overall balance across performance metrics and supports clinically interpretable localization through the prior channel. We discuss limitations related to single-cohort evaluation and the lack of large-scale public brain MRI datasets paired with radiology reports under broadly usable terms.