Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
A vision transformer for decoding surgeon activity from surgical videos
The intraoperative activity of a surgeon has substantial impact on postoperative outcomes.
However, for most surgical procedures, the details of intraoperative surgical actions, which …
However, for most surgical procedures, the details of intraoperative surgical actions, which …
Unsupervised ECG analysis: A review
Electrocardiography is the gold standard technique for detecting abnormal heart conditions.
Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the …
Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the …
In-distribution and out-of-distribution self-supervised ecg representation learning for arrhythmia detection
S Soltanieh, J Hashemi… - IEEE Journal of Biomedical …, 2023 - ieeexplore.ieee.org
This paper presents a systematic investigation into the effectiveness of Self-Supervised
Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by …
Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by …
A Generalisable Heartbeat Classifier Leveraging Self-Supervised Learning for ECG Analysis During Magnetic Resonance Imaging
PG Aublin, J Felblinger, J Oster - IEEE Journal of Biomedical …, 2024 - ieeexplore.ieee.org
Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor
patients and synchronize image acquisition with the heart motion. ECG signals are highly …
patients and synchronize image acquisition with the heart motion. ECG signals are highly …
心电领域中的自监督学习方法综述.
韩涵， 黄训华， 常慧慧， 樊好义… - Journal of Frontiers of …, 2024 - search.ebscohost.com
深度学习因其强大的数据表征能力已被广泛应用于心电(ECG) 信号分析领域,
但有监督方法的训练过程需要大量标签, 而心电数据标注通常是耗时且成本高昂的 …
但有监督方法的训练过程需要大量标签, 而心电数据标注通常是耗时且成本高昂的 …
Pcps: Patient cardiac prototypes to probe ai-based medical diagnoses, distill datasets, and retrieve patients
D Kiyasseh, T Zhu, DA Clifton - Transactions on Machine Learning …, 2023 - openreview.net
Clinical deep learning systems often generate population-based and opaque medical
diagnoses. This is in contrast to how primary care physicians make decisions, often adapting …
diagnoses. This is in contrast to how primary care physicians make decisions, often adapting …
Soqal: Selective oracle questioning for consistency based active learning of cardiac signals
D Kiyasseh, T Zhu, DA Clifton - arXiv preprint arXiv:2004.09557, 2020 - arxiv.org
Clinical settings are often characterized by abundant unlabelled data and limited labelled
data. This is typically driven by the high burden placed on oracles (eg, physicians) to provide …
data. This is typically driven by the high burden placed on oracles (eg, physicians) to provide …
Self-supervised learning for early detection of neurodegenerative diseases with small data
H Jiang - 2023 - dr.ntu.edu.sg
Neurodegenerative diseases are one of the leading causes of disability in the world. They
are chronic diseases where patients experience irreversible depletion of neurons in the …
are chronic diseases where patients experience irreversible depletion of neurons in the …