Hierarchical Attention for Sparse Volumetric Anomaly Detection in Subclinical Keratoconus

TL;DR

This paper demonstrates that hierarchical attention architectures significantly improve the detection of subtle volumetric anomalies in medical imaging, outperforming CNNs and global transformers by aligning spatial scales with abnormal regions.

Contribution

It introduces a hierarchical attention framework tailored for volumetric anomaly detection, showing its superiority over existing models in sensitivity and specificity.

Findings

Hierarchical models achieve 21-23% higher sensitivity and specificity.

Spatial scale alignment is key to effective anomaly detection.

Hierarchical attention learns a distinct, flexible feature space.

Abstract

The detection of weak, spatially distributed anomalies in volumetric medical imaging remains challenging due to the difficulty of integrating subtle signals across non-adjacent regions. This study presents a controlled comparison of sixteen architectures spanning convolutional, hybrid, and transformer families for subclinical keratoconus detection from three-dimensional anterior segment optical coherence tomography (AS-OCT). The results demonstrate that hierarchical architectures achieve 21-23% higher sensitivity and specificity, particularly in the difficult subclinical regime, outperforming both convolutional neural networks (CNNs) and global-attention Vision Transformer (ViT) baselines. Mechanistic analyses indicate that this advantage arises from spatial scale alignment: hierarchical windowing produces effective receptive fields matched to the intermediate extent of subclinical abnormalities, avoiding the excessive locality observed in convolutional models and the diffuse integration characteristic of pure global attention. Attention-distance measurements show that subclinical cases require longer spatial integration than healthy or overtly pathological volumes, with hierarchical models exhibiting lower variance and more anatomically coherent focus. Representational similarity further indicates that hierarchical attention learns a distinct feature space that balances local structure sensitivity with flexible long-range interactions. Auxiliary age and sex prediction tasks demonstrate moderately high cross-task consistency, supporting the generalizability of these inductive principles. The findings provide design guidance for volumetric anomaly detection and highlight hierarchical attention as a principled approach for early pathological change analysis in medical imaging.