Deep Learning in Cardiology
arXiv:1902.11122 · doi:10.1109/RBME.2018.2885714
Abstract
The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.
27 pages, 2 figures, 10 tables
References in corpus (8)
- A Survey on Deep Learning in Medical Image Analysis
- Medical Image Analysis using Convolutional Neural Networks: A Review
- Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
- Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
- Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
- Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks
- Estimation of the volume of the left ventricle from MRI images using deep neural networks
- Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography