L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data
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.
ProofPaper ↗
Key points
- Healthcare institutions routinely accumulate high-dimensional computed tomography (CT) volumes, but only a fraction can be annotated by experts.
- A slice encoder learns view-invariant local features, and a global encoder aggregates the resulting tokens to model volumetric context.
- 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.
- 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 $0.0954$.
Sources (1)
- [1]L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare DataarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 06:37 AM
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.
Healthcare institutions routinely accumulate high-dimensional computed tomography (CT) volumes, but only a fraction can be annotated by experts.
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