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Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis
In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a “bag” as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, c...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072016/ https://www.ncbi.nlm.nih.gov/pubmed/37363384 http://dx.doi.org/10.1007/s10489-023-04458-y |
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author | Hu, Huafeng Ye, Ruijie Thiyagalingam, Jeyan Coenen, Frans Su, Jionglong |
author_facet | Hu, Huafeng Ye, Ruijie Thiyagalingam, Jeyan Coenen, Frans Su, Jionglong |
author_sort | Hu, Huafeng |
collection | PubMed |
description | In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a “bag” as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information. |
format | Online Article Text |
id | pubmed-10072016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-100720162023-04-04 Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis Hu, Huafeng Ye, Ruijie Thiyagalingam, Jeyan Coenen, Frans Su, Jionglong Appl Intell (Dordr) Article In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a “bag” as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information. Springer US 2023-04-04 /pmc/articles/PMC10072016/ /pubmed/37363384 http://dx.doi.org/10.1007/s10489-023-04458-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Hu, Huafeng Ye, Ruijie Thiyagalingam, Jeyan Coenen, Frans Su, Jionglong Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis |
title | Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis |
title_full | Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis |
title_fullStr | Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis |
title_full_unstemmed | Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis |
title_short | Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis |
title_sort | triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072016/ https://www.ncbi.nlm.nih.gov/pubmed/37363384 http://dx.doi.org/10.1007/s10489-023-04458-y |
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