PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL
Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes of physiologically informative clinical recordings untapped. To address this limitation, we propose Physiology-aware Representation Learning via Integrated Self-supervision and Metadata for CTG (PRISM-CTG), a clinically grounded self-supervised foundation model (FM) for CTG that leverages large-scale unlabelled recordings to learn transferable domain-level representations. PRISM-CTG is pretrained using a multi-view self-supervised framework that jointly optimises 3 complementary pretext objectives: random-projec...
| Authors | Sheng Wong, Ravi Shankar, Beth Albert et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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