PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis

• URL: http://arxiv.org/abs/2607.19696v1
• Date: Wed, 22 Jul 2026 02:55:36 GMT
• Title: PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis
• Abstract summary: This study presents a Magnetic Resonance Imaging dataset of 16,813 images from 250 patients, curated to facilitate advanced deep learning research.<n>We propose a robust diagnostic benchmark that integrates state-of-theart convolutional backbones with a Positional PINE mechanism to explicitly model the anatomical context of intervertebral discs.<n>We find that multisequence fusion strategies yield inferior performance compared to this single-sequence baseline, as the images across sequences in our dataset are significantly compromised by noise interference from surrounding anatomical regions.
• Score: 4.172199727841524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse, high-quality benchmarks. In this study, we present PhenSPINE, a Magnetic Resonance Imaging dataset comprising 16,813 images from 250 patients, curated to facilitate advanced deep learning research. We propose a robust diagnostic benchmark that integrates state-of-theart convolutional backbones with a Positional Encoding mechanism to explicitly model the anatomical context of intervertebral discs. Evaluating across four standard MRI sequences, our experiments demonstrate that the Sagittal T2-weighted sequence offers the most robust diagnostic value, achieving a superior Macro F1-score of 50.31%. We find that multisequence fusion strategies yield inferior performance compared to this single-sequence baseline, as the images across sequences in our dataset are significantly compromised by noise interference from surrounding anatomical regions. This work establishes a robust baseline and offers critical insights into sequence selection for spine analysis.

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