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arXiv · 2501.03967

Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification

Satchel French·Faith Zhu·Amish Jain·Naimul Khan
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Abstract

Automated viewpoint classification in echocardiograms can help under-resourced clinics and hospitals in providing faster diagnosis and screening when expert technicians may not be available. We propose a novel approach towards echocardiographic viewpoint classification. We show that treating viewpoint classification as video classification rather than image classification yields advantage. We propose a CNN-GRU architecture with a novel temporal feature weaving method, which leverages both spatial and temporal information to yield a 4.33\% increase in accuracy over baseline image classification while using only four consecutive frames. The proposed approach incurs minimal computational overhead. Additionally, we publish the Neonatal Echocardiogram Dataset (NED), a professionally-annotated dataset providing sixteen viewpoints and associated echocardipgraphy videos to encourage future work and development in this field. Code available at: https://github.com/satchelfrench/NED

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Satchel French, Faith Zhu, Amish Jain, Naimul Khan. 2025-01-07. Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification. https://arxiv.org/abs/2501.03967

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