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This study introduces PhenSPINE, a comprehensive MRI dataset designed to enhance the automated diagnosis of spinal pathologies through deep learning. By integrating advanced convolutional architectures with a Positional Encoding mechanism, the research identifies the Sagittal T2-weighted sequence as the most effective for diagnosis, achieving a Macro F1-score of 50.31%. The findings challenge the efficacy of multisequence fusion strategies, highlighting the impact of noise interference on diagnostic performance and establishing a critical benchmark for future research in spine analysis.
The Sagittal T2-weighted MRI sequence outperforms multisequence approaches, achieving a Macro F1-score of 50.31% in spine pathology diagnosis.
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.
