Breaking away from labels: The promise of self-supervised machine learning in intelligent health

Spathis, Dimitris; Perez-Pozuelo, Ignacio; Marques-Fernandez, Laia; Mascolo, Cecilia

doi:10.1016/j.patter.2021.100410

Cited by 23 publications

(14 citation statements)

References 12 publications

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“…Having a multi-modal representation would allow us to build foundation models that could be used in critical fields like health research (Bommasani et al, 2021;Spathis et al, 2022). Another potential work is the analysis of inter-subject and intra-subject variability in sensor data which could better inform the data curation procedures.…”

Section: Discussionmentioning

confidence: 99%

Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data

Yuan¹,

Chan²,

Creagh³

et al. 2022

Preprint

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Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage selfsupervised learning techniques on the UK-Biobank activity tracker dataset-the largest of its kind to date-containing more than 700,000 person-days of unlabelled wearable sensor data. Our resulting activity recognition model consistently outperformed strong baselines across seven benchmark datasets, with an F1 relative improvement of 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, and environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance.

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Section: Discussionmentioning

confidence: 99%

Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data

Yuan¹,

Chan²,

Creagh³

et al. 2022

Preprint

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“…They validate three medical problems: chest radiography abnormality assessment, brain metastasis detection in MR, and brain hemorrhage detection in CT image data. Spathis et al [ 45 ] discussed the role of self-supervised learning in the medical domain. They collected ECG signal data and applied prominent SSL methods to it.…”

Section: Self-supervised Learning (Ssl)mentioning

confidence: 99%

Self-supervised Learning: A Succinct Review

Rani

Nabi

Kumar

et al. 2023

Arch Computat Methods Eng

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Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article.

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“… - Self-supervised (machine learning) A subset of unsupervised machine learning algorithms that are able to take on tasks which are traditionally tackled by supervised machine learning, without using data labels. Spathis et al [81] Sparsity (of features) The number of features with zero values. - Supervised (machine learning) A machine learning approach that uses a labeled dataset to improve the its prediction of outcomes.…”

Section: Encoding Enzymes As Features For Machine Learningmentioning

confidence: 99%

Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data

Lim

Julca

Mutwil

2023

Computational and Structural Biotechnology Journal

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Breaking away from labels: The promise of self-supervised machine learning in intelligent health

Cited by 23 publications

References 12 publications

Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data

Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data

Self-supervised Learning: A Succinct Review

Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data

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