TY - GEN
T1 - Contrastive Learning for Echocardiographic View Integration
AU - Cheng, Li Hsin
AU - Sun, Xiaowu
AU - van der Geest, Rob J.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this work, we aimed to tackle the challenge of fusing information from multiple echocardiographic views, mimicking cardiologists making diagnoses with an integrative approach. For this purpose, we used the available information provided in the CAMUS dataset to experiment combining 2D complementary views to derive 3D information of left ventricular (LV) volume. We proposed intra-subject and inter-subject volume contrastive losses with varying margin to encode heterogeneous input views to a shared view-invariant volume-relevant feature space, where feature fusion can be facilitated. The results demonstrated that the proposed contrastive losses successfully improved the integration of complementary information from the input views, achieving significantly better volume predictive performance (MAE: 10.96 ml, RMSE: 14.75 ml, R2: 0.88) than that of the late-fusion baseline without contrastive losses (MAE: 13.17 ml, RMSE: 17.91 ml, R2: 0.83). Code available at: https://github.com/LishinC/VCN.
AB - In this work, we aimed to tackle the challenge of fusing information from multiple echocardiographic views, mimicking cardiologists making diagnoses with an integrative approach. For this purpose, we used the available information provided in the CAMUS dataset to experiment combining 2D complementary views to derive 3D information of left ventricular (LV) volume. We proposed intra-subject and inter-subject volume contrastive losses with varying margin to encode heterogeneous input views to a shared view-invariant volume-relevant feature space, where feature fusion can be facilitated. The results demonstrated that the proposed contrastive losses successfully improved the integration of complementary information from the input views, achieving significantly better volume predictive performance (MAE: 10.96 ml, RMSE: 14.75 ml, R2: 0.88) than that of the late-fusion baseline without contrastive losses (MAE: 13.17 ml, RMSE: 17.91 ml, R2: 0.83). Code available at: https://github.com/LishinC/VCN.
KW - Contrastive learning
KW - Echocardiogram
KW - Left ventricular volume regression
KW - Multi-view integration
UR - https://www.scopus.com/pages/publications/85139018913
U2 - 10.1007/978-3-031-16440-8_33
DO - 10.1007/978-3-031-16440-8_33
M3 - Conference Proceeding
AN - SCOPUS:85139018913
SN - 9783031164392
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 340
EP - 349
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -