Investigations on self-supervised learning strategies for diagnosis and prognosis of vertebral fractures in CT images

Abstract

Self-supervised learning (SSL) promises label-efficient pretraining, but its benefit for fine-grained vertebral fracture grading from CT is unclear. This thesis evaluates whether SSL pretraining improves Genant semi-quantitative grading on vertebra-centered CT crops under limited labeled data. A supervised SE-ResNet-50 baseline is compared against BYOL using two transfer protocols: linear evaluation and fine-tuning. Across diagnostic experiments, linear evaluation performs substantially worse than supervised training, especially for multi-class fracture grading, indicating that pretrained features are not readily linearly separable for severity distinctions. Fine-tuning largely closes this gap and achieves competitive performance, suggesting SSL mainly provides an initialization rather than a feature extractor. Error analysis shows reliable predictions for healthy vertebrae but unstable predictions for severe fracture grades. Ablations on pretraining duration and unlabeled data composition affect linear evaluation but translate weakly to fine-tuning gains. A prognosis reproduction indicates SSL can support patient-level risk ranking, but improvements are task-dependent