ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function

Ethan Thomas, Salman Aslam
12/13/2023
eess.IVcs.CVcs.LG

Official code: Aether111/ConFormer (4 stars, MIT, last commit 2023-11-08).

Abstract

Cardiovascular diseases, particularly heart failure, are a leading cause of death globally. The early detection of heart failure through routine echocardiogram screenings is often impeded by the high cost and labor-intensive nature of these procedures, a barrier that can mean the difference between life and death. This paper presents ConFormer, a novel deep learning model designed to automate the estimation of Ejection Fraction (EF) and Left Ventricular Wall Thickness from echocardiograms. The implementation of ConFormer has the potential to enhance preventative cardiology by enabling cost-effective, accessible, and comprehensive heart health monitoring, thereby saving countless lives. The source code is available at https://github.com/Aether111/ConFormer.

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Code Implementations(1)

Aether111/ConFormerOfficial100%
MIT

Model Code Implementation in Keras from the ConFormer Research Paper

40PythonNov 6, 20232 years agoMIT

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Cite this paper

@article{thomas2023conformer,
  title  = {ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function},
  author = {Ethan Thomas and Salman Aslam},
  year   = {2023},
  eprint = {2312.08567},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.08567v2}
}

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