BILATERAL ATTENTION-BASED CLASSIFICATION OF SPINAL DEGENERATIVE DISEASE PATTERNS FROM LUMBAR MRI SLICES

Abstract

This study aims to develop a bilateral attention-based deep learning framework for automated classification of lumbar degenerative disease patterns from MRI. Lumbar spine degenerative diseas-es represent a leading cause of chronic low back pain and functional disability, entailing precise MRI-based assessment for timely diagnosis and treatment planning. Automated classification remains difficult due to subtle, localized abnormalities, a considerable reliance on MRI views, and the need to integrate information across adjacent slices and bilateral anatomical structures. This study reports a bilateral attention-based deep learning framework for localized lumbar degenerative dis-ease classification from MRI. The approach reformulates the RSNA 2024 Lumbar Spine Degenera-tive Classification benchmark as an ROI-centered binary pathology detection task utilizing expert-provided lesion coordinates. Each sample contains a stack of five adjacent MRI slices centered on the target pathology. A shared ResNet-18 encoder extracts slice-wise visual features, which are aggregated via attention-based multi-slice pooling and fused with structured anatomical metadata—including lumbar level, pathology type, MRI view, and side-aware bilateral context—using a Trans-former encoder. Experimental evaluation demonstrates strong discriminative performance, achieving an AUROC of 0.952 on the validation data, with particularly strong results for spinal canal stenosis. The proposed method outperforms the simple ResNet-18 baseline by 7% and the variant without bilateral attention by 1%. These results illustrate the effectiveness of pathology-centered ROI learn-ing, slice-level attention, and bilateral contextual fusion for reliable computer-aided lumbar MRI di-agnosis.

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