Quality controlling in capsule gastroduodenoscopy with less annotation via self-supervised learning Article Swipe
Yaqiong Zhang
,
Kai Zhang
,
Meijia Wang
,
Peng Bai
,
Shengqiang Wang
,
Ting Ma
,
Feng Hu
,
Peng Li
,
Guisheng Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6324281/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6324281/v1
Background It is possible to control the quality of capsule endoscopic images using artificial intelligence (AI), but it requires a great deal of time for labeling. Methods SimCLR (a simple framework for contrastive learning of visual representations), is capable of acquiring the inherent image representation with minimal annotation, but the feasibility is not studied. 62850 images were collected to train models. In internal cross-validation (more training data and less testing data) and reversed cross-validation (less training data and more testing data). Random forest and Xgboost (eXtreme Gradient Boosting) were used to finish the quality controlling after SimCLR extracting the features from images. Results SimCLR reported that the mean AUROC (Area Under the Receiver Operating Characteristic) curve exceeded 0.98 and 0.97. Moreover, Xgboost surpassed supervised CNN (Convolutional Neural Network). Extra 18636 pictures were gathered and the AUROC of SimCLR surpassed 0.93 (95% CI 0.9271–0.9548), which is close to supervised CNN (Convolutional Neural Network) (0.9645) in cross validation. Moreover, the AUROC of SimCLR surpass 0.96, which is better than supervised CNN (0.8374) in reversed cross validation. Conclusions Through SimCLR, the capsule endoscopic image quality control task can be completed with a performance similar to or better than that of supervised learning with fewer annotations.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6324281/v1
- https://www.researchsquare.com/article/rs-6324281/latest.pdf
- OA Status
- gold
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410239073
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410239073Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-6324281/v1Digital Object Identifier
- Title
-
Quality controlling in capsule gastroduodenoscopy with less annotation via self-supervised learningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-09Full publication date if available
- Authors
-
Yaqiong Zhang, Kai Zhang, Meijia Wang, Peng Bai, Shengqiang Wang, Ting Ma, Feng Hu, Peng Li, Guisheng LiuList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-6324281/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-6324281/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-6324281/latest.pdfDirect OA link when available
- Concepts
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Capsule, Annotation, Computer science, Artificial intelligence, Quality (philosophy), Biology, Physics, Botany, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.data) | 71 |
| abstract_inverted_index.fewer | 201 |
| abstract_inverted_index.great | 21 |
| abstract_inverted_index.image | 44, 181 |
| abstract_inverted_index.train | 60 |
| abstract_inverted_index.using | 13 |
| abstract_inverted_index.which | 144, 164 |
| abstract_inverted_index.Neural | 127, 151 |
| abstract_inverted_index.Random | 82 |
| abstract_inverted_index.SimCLR | 28, 97, 104, 138, 161 |
| abstract_inverted_index.better | 166, 194 |
| abstract_inverted_index.data). | 81 |
| abstract_inverted_index.finish | 92 |
| abstract_inverted_index.forest | 83 |
| abstract_inverted_index.images | 12, 56 |
| abstract_inverted_index.simple | 30 |
| abstract_inverted_index.visual | 36 |
| abstract_inverted_index.Methods | 27 |
| abstract_inverted_index.Results | 103 |
| abstract_inverted_index.SimCLR, | 177 |
| abstract_inverted_index.Through | 176 |
| abstract_inverted_index.Xgboost | 85, 122 |
| abstract_inverted_index.capable | 39 |
| abstract_inverted_index.capsule | 10, 179 |
| abstract_inverted_index.control | 6, 183 |
| abstract_inverted_index.images. | 102 |
| abstract_inverted_index.minimal | 47 |
| abstract_inverted_index.models. | 61 |
| abstract_inverted_index.quality | 8, 94, 182 |
| abstract_inverted_index.similar | 191 |
| abstract_inverted_index.surpass | 162 |
| abstract_inverted_index.testing | 70, 80 |
| abstract_inverted_index.(0.8374) | 170 |
| abstract_inverted_index.(0.9645) | 153 |
| abstract_inverted_index.(eXtreme | 86 |
| abstract_inverted_index.Gradient | 87 |
| abstract_inverted_index.Network) | 152 |
| abstract_inverted_index.Receiver | 113 |
| abstract_inverted_index.exceeded | 117 |
| abstract_inverted_index.features | 100 |
| abstract_inverted_index.gathered | 133 |
| abstract_inverted_index.inherent | 43 |
| abstract_inverted_index.internal | 63 |
| abstract_inverted_index.learning | 34, 199 |
| abstract_inverted_index.pictures | 131 |
| abstract_inverted_index.possible | 4 |
| abstract_inverted_index.reported | 105 |
| abstract_inverted_index.requires | 19 |
| abstract_inverted_index.reversed | 73, 172 |
| abstract_inverted_index.studied. | 54 |
| abstract_inverted_index.training | 66, 76 |
| abstract_inverted_index.Boosting) | 88 |
| abstract_inverted_index.Moreover, | 121, 157 |
| abstract_inverted_index.Network). | 128 |
| abstract_inverted_index.Operating | 114 |
| abstract_inverted_index.acquiring | 41 |
| abstract_inverted_index.collected | 58 |
| abstract_inverted_index.completed | 187 |
| abstract_inverted_index.framework | 31 |
| abstract_inverted_index.labeling. | 26 |
| abstract_inverted_index.surpassed | 123, 139 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.artificial | 14 |
| abstract_inverted_index.endoscopic | 11, 180 |
| abstract_inverted_index.extracting | 98 |
| abstract_inverted_index.supervised | 124, 148, 168, 198 |
| abstract_inverted_index.Conclusions | 175 |
| abstract_inverted_index.annotation, | 48 |
| abstract_inverted_index.contrastive | 33 |
| abstract_inverted_index.controlling | 95 |
| abstract_inverted_index.feasibility | 51 |
| abstract_inverted_index.performance | 190 |
| abstract_inverted_index.validation. | 156, 174 |
| abstract_inverted_index.annotations. | 202 |
| abstract_inverted_index.intelligence | 15 |
| abstract_inverted_index.(Convolutional | 126, 150 |
| abstract_inverted_index.representation | 45 |
| abstract_inverted_index.Characteristic) | 115 |
| abstract_inverted_index.cross-validation | 64, 74 |
| abstract_inverted_index.0.9271–0.9548), | 143 |
| abstract_inverted_index.representations), | 37 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.23746348 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |