Dense lead contrast for self-supervised representation learning of multilead electrocardiograms

Liu, Wenhan; Li, Zhoutong; Zhang, Huaicheng; Chang, Sheng; Wang, Hao; He, Jin; Huang, Qijun

doi:10.1016/j.ins.2023.03.099

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…If a model consistently returns the same representations for differently cropped versions of the same image, it can effectively remove any spatial information about the image and will likely perform poorly in tasks such as semantic segmentation and object detection, which rely on this spatial information. Dense contrastive learning (DCL) has been proposed and considered by various researchers to address this issue [54][55][56][57]. Rather than utilizing contrastive loss on the entire image, it was applied to individual patches.…”

Section: Contrastive Ssl Paradigmsmentioning

confidence: 99%

Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts

Abdulrazzaq,

Ramaha,

Hameed

et al. 2024

Mathematics

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Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review’s numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy.

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Section: Contrastive Ssl Paradigmsmentioning

confidence: 99%