Hierarchical Vision Transformers for Cardiac Ejection Fraction Estimation

Lhuqita Fazry, Asep Haryono, Nuzulul Khairu Nissa, Sunarno, Naufal Muhammad Hirzi, Muhammad Febrian Rachmadi, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages39-44
Number of pages6
ISBN (Electronic)9781665489508
DOIs
Publication statusPublished - 2022
Event7th International Workshop on Big Data and Information Security, IWBIS 2022 - Depok, Indonesia
Duration: 1 Oct 20223 Oct 2022

Publication series

NameIWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings

Conference

Conference7th International Workshop on Big Data and Information Security, IWBIS 2022
Country/TerritoryIndonesia
CityDepok
Period1/10/223/10/22

Keywords

  • Cardiac Ejection Fraction
  • Echocardiography
  • EchoNet-Dynamic
  • UltraSwin
  • Vision Transformers

Fingerprint

Dive into the research topics of 'Hierarchical Vision Transformers for Cardiac Ejection Fraction Estimation'. Together they form a unique fingerprint.

Cite this