Open-world active learning for echocardiography view classification

Ghada Zamzmi; Tochi Oguguo; Sivaramakrishnan Rajaraman; Sameer Antani

doi:10.1117/12.2612578

4 April 2022 Open-world active learning for echocardiography view classification

Ghada Zamzmi, Tochi Oguguo, Sivaramakrishnan Rajaraman, Sameer Antani

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120330J (2022) https://doi.org/10.1117/12.2612578
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Existing works for automated echocardiography view classification are designed under the assumption that the classes (views) in the testing set must be similar to those appeared in the training set (closed world classification). This assumption may be too strict for real-world environments that are open and often have unseen examples (views), thereby drastically weakening the robustness of the classical 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 an echocardiologist. 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.

Conference Presentation

Citation Download Citation

Ghada Zamzmi, Tochi Oguguo, Sivaramakrishnan Rajaraman, and Sameer Antani "Open-world active learning for echocardiography view classification", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120330J (4 April 2022); https://doi.org/10.1117/12.2612578

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available