arXiv:1812.10386
공식 코드 · Papers with Code
ECG Segmentation by Neural Networks: Errors and Correction
원문 초록
In this study we examined the question of how error correction occurs in an ensemble of deep convolutional networks, trained for an important applied problem: segmentation of Electrocardiograms(ECG). We also explore the possibility of using the information about ensemble errors to evaluate a quality of data representation, built by the network. This possibility arises from the effect of distillation of outliers, which was demonstarted for the ensemble, described in this paper.
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Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection
Are ECGs enough? Deep learning classification of cardiac anomalies using only electrocardiograms
MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach
Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study AI for ECG to CMR Translation Study
Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture
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