Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learning

• URL: http://arxiv.org/abs/2509.20813v1
• Date: Thu, 25 Sep 2025 06:52:25 GMT
• Title: Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learning
• Abstract summary: Low back pain affects millions worldwide, driving the need for robust diagnostic models.<n>We present LumbarCLIP, a novel framework that leverages contrastive language-image pretraining to align lumbar spine MRI scans with corresponding radiological descriptions.
• Score: 0.3499870393443268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low back pain affects millions worldwide, driving the need for robust diagnostic models that can jointly analyze complex medical images and accompanying text reports. We present LumbarCLIP, a novel multimodal framework that leverages contrastive language-image pretraining to align lumbar spine MRI scans with corresponding radiological descriptions. Built upon a curated dataset containing axial MRI views paired with expert-written reports, LumbarCLIP integrates vision encoders (ResNet-50, Vision Transformer, Swin Transformer) with a BERT-based text encoder to extract dense representations. These are projected into a shared embedding space via learnable projection heads, configurable as linear or non-linear, and normalized to facilitate stable contrastive training using a soft CLIP loss. Our model achieves state-of-the-art performance on downstream classification, reaching up to 95.00% accuracy and 94.75% F1-score on the test set, despite inherent class imbalance. Extensive ablation studies demonstrate that linear projection heads yield more effective cross-modal alignment than non-linear variants. LumbarCLIP offers a promising foundation for automated musculoskeletal diagnosis and clinical decision support.

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