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Learning Self-Super...
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images
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- Gupta, Ekta (författare)
- Chandigarh College of Engineering and Technology, Punjab University, Chandigarh, India
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- Gupta, Varun (författare)
- Chandigarh College of Engineering and Technology, Punjab University, Chandigarh, India
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- Chopra, Muskaan (författare)
- Chandigarh College of Engineering and Technology, Punjab University, Chandigarh, India
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- Chhipa, Prakash Chandra (författare)
- Luleå tekniska universitet,EISLAB
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- Liwicki, Marcus (författare)
- Luleå tekniska universitet,EISLAB
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2023
- 2023
- Engelska.
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Ingår i: IJCNN 2023 - International Joint Conference on Neural Networks, Conference Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665488686 - 9781665488679
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- This work presents a novel label-efficient self-supervised representation learning-based approach for classifying diabetic retinopathy (DR) images in cross-domain settings. Most of the existing DR image classification methods are based on supervised learning which requires a lot of time-consuming and expensive medical domain experts-annotated data for training. The proposed approach uses the prior learning from the source DR image dataset to classify images drawn from the target datasets. The image representations learned from the unlabeled source domain dataset through contrastive learning are used to classify DR images from the target domain dataset. Moreover, the proposed approach requires a few labeled images to perform successfully on DR image classification tasks in cross-domain settings. The proposed work experiments with four publicly available datasets: EyePACS, APTOS 2019, MESSIDOR-I, and Fundus Images for self-supervised representation learning-based DR image classification in cross-domain settings. The proposed method achieves state-of-the-art results on binary and multi-classification of DR images, even in cross-domain settings. The proposed method outperforms the existing DR image binary and multi-class classification methods proposed in the literature. The proposed method is also validated qualitatively using class activation maps, revealing that the method can learn explainable image representations. The source code and trained models are published on GitHub11https://github.com/prakashchhipa/Learning-Self-Supervised-Representations-for-Label-Efficient-Cross-Domain-Knowledge-Transfer-on-DRF.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- domain adaptation
- Self-supervised representation learning
- Maskininlärning
- Machine Learning
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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