Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916554970497024 |
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| author | French, Satchel Zhu, Faith Jain, Amish Khan, Naimul |
| author_facet | French, Satchel Zhu, Faith Jain, Amish Khan, Naimul |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03967 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification French, Satchel Zhu, Faith Jain, Amish Khan, Naimul Computer Vision and Pattern Recognition 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 |
| title | Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.03967 |