TY - GEN
T1 - Hierarchical Vision Transformers for Cardiac Ejection Fraction Estimation
AU - Fazry, Lhuqita
AU - Haryono, Asep
AU - Nissa, Nuzulul Khairu
AU - Sunarno,
AU - Hirzi, Naufal Muhammad
AU - Rachmadi, Muhammad Febrian
AU - Jatmiko, Wisnu
N1 - Funding Information:
This work is supported by Research Laboratory of Faculty of Computer Science, Universitas Indonesia. Thank you for contributing to provide some facilities in laboratory and supporting this research.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The left ventricular of ejection fraction is one of the most important metric of cardiac function. It is used by cardiologist to identify patients who are eligible for life-prolonging therapies. However, the assessment of ejection fraction suffers from inter-observer variability. To overcome this challenge, we propose a deep learning approach, based on hierarchical vision Transformers, to estimate the ejection fraction from echocardiogram videos. The proposed method can estimate ejection fraction without the need for left ventrice segmentation first, make it more efficient than other methods. We evaluated our method on EchoNet-Dynamic dataset resulting 5.59, 7.59 and 0.59 for MAE, RMSE and R2 respectivelly. This results are better compared to the state-of-the-art method, Ultrasound Video Transformer (UVT). The source code is available on https://github.com/lhfazry/UltraSwin.
AB - The left ventricular of ejection fraction is one of the most important metric of cardiac function. It is used by cardiologist to identify patients who are eligible for life-prolonging therapies. However, the assessment of ejection fraction suffers from inter-observer variability. To overcome this challenge, we propose a deep learning approach, based on hierarchical vision Transformers, to estimate the ejection fraction from echocardiogram videos. The proposed method can estimate ejection fraction without the need for left ventrice segmentation first, make it more efficient than other methods. We evaluated our method on EchoNet-Dynamic dataset resulting 5.59, 7.59 and 0.59 for MAE, RMSE and R2 respectivelly. This results are better compared to the state-of-the-art method, Ultrasound Video Transformer (UVT). The source code is available on https://github.com/lhfazry/UltraSwin.
KW - Cardiac Ejection Fraction
KW - Echocardiography
KW - EchoNet-Dynamic
KW - UltraSwin
KW - Vision Transformers
UR - http://www.scopus.com/inward/record.url?scp=85141833012&partnerID=8YFLogxK
U2 - 10.1109/IWBIS56557.2022.9924664
DO - 10.1109/IWBIS56557.2022.9924664
M3 - Conference contribution
AN - SCOPUS:85141833012
T3 - IWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings
SP - 39
EP - 44
BT - IWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Workshop on Big Data and Information Security, IWBIS 2022
Y2 - 1 October 2022 through 3 October 2022
ER -