Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
Congenital Heart Disease (CHD) is one of the leading causes of fetal
mortality, yet the scarcity of labeled CHD data and strict privacy regulations
surrounding fetal ultrasound (US) imaging present significant challenges for
the development of deep learning-based models for CHD detection. Centralised
collection of large real-world datasets for rare conditions, such as CHD, from
large populations requires significant co-ordination and resource. In addition,
data governance rules increasingly prevent data sharing between sites. To
address these challenges, we introduce, for the first time, a novel
privacy-preserving, zero-shot CHD detection framework that formulates CHD
detection as a normality modeling problem integrated with model merging. In our
framework dubbed Sparse Tube Ultrasound Distillation (STUD), each hospital site
first trains a sparse video tube-based self-supervised video anomaly detection
(VAD) model on normal fetal heart US clips with self-distillation loss. This
enables site-specific models to independently learn the distribution of healthy
cases. To aggregate knowledge across the decentralized models while maintaining
privacy, we propose a Divergence Vector-Guided Model Merging approach,
DivMerge, that combines site-specific models into a single VAD model without
data exchange. Our approach preserves domain-agnostic rich spatio-temporal
representations, ensuring generalization to unseen CHD cases. We evaluated our
approach on real-world fetal US data collected from 5 hospital sites. Our
merged model outperformed site-specific models by 23.77% and 30.13% in accuracy
and F1-score respectively on external test sets.
