Aggregative Self-supervised Feature Learning from Limited Medical Images

Zhu, Jiuwen; Li, Yuexiang; Ding, Lixin; Zhou, S. Kevin

doi:10.1007/978-3-031-16452-1_6

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The diversity of distribution of the histology dataset makes little difference for linear tumour classification on histopathology images. Taleb et al [131] extended this idea to a 3D CPC version. Instead of the time sequence dataset used in CPC, 3D CPC utilized a feature representation set obtained from patches cropped from the upper or left part of the 2D image sample to predict the encoded feature representations of the remaining part, lower or right part.…”

Section: Context-instance Contrast Learning For Medical Image Analysi...mentioning

confidence: 99%

“…NLST dataset LUNA 6 dataset SPIE-AAPM dataset Lung TIME dataset HMS Lung cancer dataset Pulmonary nodule classification Dong et al [114] , 2021 CT images dataset Focal liver lesions classification Koohbanani et al [119] , 2020 Stacke et al [130] , 2020 STL-10 CAMELYON17 AIDA-LNCO AIDA-SKIN [Histopathological images] Tumor classification Taleb et al [131] , 2020 [132] , 2021 3D brain hemorrhage dataset (private dataset) Brain hemorrhage classification Zhu et al [134] , 2020 Brain hemorrhage dataset (private dataset) LUNA16 dataset…”

Section: Retinal Disease Classificationmentioning

confidence: 99%

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A Review of Predictive and Contrastive Self-supervised Learning for Medical Images

et al. 2023

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Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.

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Section: Context-instance Contrast Learning For Medical Image Analysi...mentioning

confidence: 99%

Section: Retinal Disease Classificationmentioning

confidence: 99%

A Review of Predictive and Contrastive Self-supervised Learning for Medical Images

et al. 2023

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Modeling Alzheimers’ Disease Progression from Multi-task and Self-supervised Learning Perspective with Brain Networks

Liang,

Zhang,

Cao

et al. 2023

Lecture Notes in Computer Science

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A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

VanBerlo,

Hoey,

Wong

2024

BMC Med Imaging

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Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.

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Aggregative Self-supervised Feature Learning from Limited Medical Images

Cited by 3 publications

References 14 publications

A Review of Predictive and Contrastive Self-supervised Learning for Medical Images

A Review of Predictive and Contrastive Self-supervised Learning for Medical Images

Modeling Alzheimers’ Disease Progression from Multi-task and Self-supervised Learning Perspective with Brain Networks

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

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