mlsanityData to 7 Oct 2026

Disc-Centric Contrastive Learning for Lumbar Spine Severity Grading

arXiv 2602.05738 ↗First posted 5 Feb 2026eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

This work examines a disc-centric approach for automated severity grading of lumbar spinal stenosis from sagittal T2-weighted MRI. The method combines contrastive pretraining with disc-level fine-tuning, using a single anatomically localized region of interest per intervertebral disc. Contrastive learning is employed to help the model focus on meaningful disc features and reduce sensitivity to irrelevant differences in image appearance. The framework includes an auxiliary regression task for disc localization and applies weighted focal loss to address class imbalance. Experiments demonstrate a 78.1% balanced accuracy and a reduced severe-to-normal misclassification rate of 2.13% compared with supervised training from scratch. Detecting discs with moderate severity can still be challenging, but focusing on disc-level features provides a practical way to assess the lumbar spinal stenosis.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 5 Feb 2026.

Builds on

9 of 23 references.

References · 9

  1. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  2. 2020Supervised Contrastive Learningcited by 2,803
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  4. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  5. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  6. 2025The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Datasetcited by 6
  7. 2023CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstructioncited by 4
  8. 2025AI-Driven MRI Spine Pathology Detection: A Comprehensive Deep Learning Approach for Automated Diagnosis in Diverse Clinical Settingscited by 2
  9. 2025Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learningcited by 1

Built on by

Not cited yet.