PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis
Organizations: Business AI Lab, College of Technology, National Economics University, Vietnam · Medical Imaging & Radiological Technology Department, Faculty of Medical Technology, Phenikaa School of Medicine & Pharmacy, Phenikaa University, Vietnam · Radiology and Functional Exploration Center, Phenikaa University Hospital, Vietnam · A2I Lab, Phenikaa School of Computing, Phenikaa University, Hanoi, Vietnam
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.