Open-world active learning for echocardiography view classification.

Oguguo T, Zamzmi G, Rajaraman S, Antani S

SPIE Medical Imaging, (February 2022).


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.

Oguguo T, Zamzmi G, Rajaraman S, Antani S. Open-world active learning for echocardiography view classification. 
SPIE Medical Imaging, (February 2022).