L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data

arXiv:2610.21356v1 Announce Type: new Abstract: Healthcare institutions routinely accumulate high-dimensional computed tomography (CT) volumes, but only a fraction can be annotated by experts. We present L2GSSL, a local-to-global self-supervised learning framework that leverages unlabeled volumes by decomposing pretraining into intra-slice and inter-slice representation spaces. A slice encoder learns view-invariant local features, and a global encoder aggregates the resulting tokens to model volumetric context. The stop-gradient isolates the local space from the global objective, while slice-wise execution and a three-phase schedule support training under constrained GPU memory. On LIDC-IDRI lung nodule malignancy classification, L2GSSL reaches 0.9229 AUROC with only 10% of the labels and 0.9377 with 25%, exceeding the volume-level VICReg by 0.0229 and 0.0294, respectively. With all labels, the strongest supervised baseline exceeds L2GSSL by only 0.0044 AUROC, consistent with self-supervised pretraining providing its greatest value when annotations are limited. Without adaptation to the external LNDb cohort, a model fine-tuned on 25% of LIDC-IDRI obtains 0.8138 AUROC and 0.7589 AUPRC, improving over the strongest evaluated self-supervised baseline by 0.1130 and 0.0954. Experiments across data sources, thoracic and abdominal anatomy, downstream tasks, and two backbones examine healthcare-data variety, while lesion-slice masking shows that the predictions depend on lesion-bearing content.

Source: arXiv cs.CV | 2026-10-16