CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13072 > Article
Paper
3 April 2024 Embracing uncertainty flexibility: harnessing a supervised tree kernel to empower ensemble modelling for 2D echocardiography-based prediction of right ventricular volume
Tuan A. Bohoran, Polydoros N. Kampaktsis, Laura McLaughlin, Jay Leb, Serafeim Moustakidis, Gerry P. McCann, Archontis Giannakidis
Author Affiliations +
Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 1307214 (2024) https://doi.org/10.1117/12.3023433
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia
Abstract
The right ventricular (RV) function deterioration strongly predicts clinical outcomes in numerous circumstances. To boost the clinical deployment of ensemble regression methods that quantify RV volumes using tabular data from the widely available two-dimensional echocardiography (2DE), we propose to complement the volume predictions with uncertainty scores. To this end, we employ an instance-based method which uses the learned tree structure to identify the nearest training samples to a target instance and then uses a number of distribution types to more flexibly model the output. The probabilistic and point-prediction performances of the proposed framework are evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and end-systolic RV volumes. The reference values for point performance were obtained from MRI. The results demonstrate that our flexible approach yields improved probabilistic and point performances over other state-of-the art methods. The appropriateness of the proposed framework is showcased by providing exemplar cases. The estimated uncertainty embodies both aleatoric and epistemic types. This work aligns with trustworthy artificial intelligence since it can be used to enhance the decision-making process and reduce risks. The feature importance scores of our framework can be exploited to reduce the number of required 2DE views which could enhance the proposed pipeline’s clinical application.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Tuan A. Bohoran, Polydoros N. Kampaktsis, Laura McLaughlin, Jay Leb, Serafeim Moustakidis, Gerry P. McCann, and Archontis Giannakidis "Embracing uncertainty flexibility: harnessing a supervised tree kernel to empower ensemble modelling for 2D echocardiography-based prediction of right ventricular volume", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 1307214 (3 April 2024); https://doi.org/10.1117/12.3023433
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 8 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Machine learning
Education and training
Modeling
Calibration
Performance modeling
Statistical analysis
Cardiovascular magnetic resonance imaging
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top