ArticleProceedings of SPIE--the International Society for Optical Engineering
Open World Active Learning for Echocardiography View Classification.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Impact of Simulated Artifacts on the Classification Performance of Apical Views in Transthoracic Echocardiography Using Convolutional Neural Networks.Bioengineering (Basel, Switzerland) · 2026Article
- Data Characterization for Reliable AI in Medicine.Recent trends in image processing and pattern recognition : 5th International Conference, RTIP2R 2022, Kingsville, TX, USA, December 01-02, 2022, revised selected papers. International Conference on Recent Trends in Image Processing and... · 2023Article
- Assisted probe guidance in cardiac ultrasound: A review.Frontiers in cardiovascular medicine · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Existing works for automated echocardiography view classification are designed under the assumption that the views in the testing set must belong to a limited number of views that have appeared in the training set. Such a design is called closed world classification. This assumption may be too strict for real-world environments that are open and often have unseen examples, drastically weakening the robustness of the classical view classification approaches. In this work, we developed an open world active learning approach for echocardiography view classification, where the network classifies images of known views into their respective classes and identifies images of unknown views. Then, a clustering approach is used to cluster the unknown views into various groups to be labeled by echocardiologists. Finally, the new labeled samples are added to the initial set of known views and used to update the classification network. This process of actively labeling unknown clusters and integrating them into the classification model significantly increases the efficiency of data labeling and the robustness of the classifier. Our results using an echocardiography dataset containing known and unknown views showed the superiority of the proposed approach as compared to the closed world view classification approaches.
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Registered trials
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