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cs.LG2026
PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL
Sheng Wong, Ravi Shankar, Beth Albert +5
Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes…
cs.LG2025
Predicting Fetal Outcomes from Cardiotocography Signals Using a Supervised Variational Autoencoder
John Tolladay, Beth Albert, Gabriel Davis Jones
Objective: To develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes, addressing interpre…
cs.LG2025
Large language models surpass domain-specific architectures for antepartum electronic fetal monitoring analysis
Sheng Wong, Ravi Shankar, Beth Albert +1
Foundation models (FMs) and large language models (LLMs) have demonstrated promising generalization across diverse domains for time-series analysis, yet their potential for electro…