Automated classification of polyps using deep learning architectures and few-shot learning Article Swipe
Adrian Krenzer
,
Stefan Heil
,
Daniel Fitting
,
Safa Matti
,
Wolfram G. Zoller
,
Alexander Hann
,
Frank Puppe
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2106189/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2106189/v1
Background : Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. Methods : We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. Results : For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. Conclusion : Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2106189/v1
- https://www.researchsquare.com/article/rs-2106189/latest.pdf
- OA Status
- green
- Cited By
- 6
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4301373906
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4301373906Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2106189/v1Digital Object Identifier
- Title
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Automated classification of polyps using deep learning architectures and few-shot learningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-04Full publication date if available
- Authors
-
Adrian Krenzer, Stefan Heil, Daniel Fitting, Safa Matti, Wolfram G. Zoller, Alexander Hann, Frank PuppeList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2106189/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-2106189/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-2106189/latest.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Nice, Computer science, Contextual image classification, Pattern recognition (psychology), One-class classification, Deep learning, Machine learning, Image (mathematics), Support vector machine, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2889646458, https://openalex.org/W3008225014, https://openalex.org/W3011484919, https://openalex.org/W2930562923, https://openalex.org/W2588184175, https://openalex.org/W2943735948, https://openalex.org/W2979524736, https://openalex.org/W2584038571, https://openalex.org/W6600195515, https://openalex.org/W2086327306, https://openalex.org/W2508524349, https://openalex.org/W3203505405, https://openalex.org/W2560014990, https://openalex.org/W2765527079, https://openalex.org/W2778113922, https://openalex.org/W2942907788, https://openalex.org/W3034942609, https://openalex.org/W1583837637, https://openalex.org/W1981276685, https://openalex.org/W2607662938, https://openalex.org/W2015186727, https://openalex.org/W2526468814, https://openalex.org/W4210595379, https://openalex.org/W146900863, https://openalex.org/W1036172382, https://openalex.org/W3094502228, https://openalex.org/W2062665794, https://openalex.org/W2074977333, https://openalex.org/W2997972888, https://openalex.org/W2963341924, https://openalex.org/W2969985801, https://openalex.org/W3046240927, https://openalex.org/W2047643928, https://openalex.org/W2955425717, https://openalex.org/W4239510810, https://openalex.org/W3117450517, https://openalex.org/W2122538988, https://openalex.org/W2969656782, https://openalex.org/W2801958409, https://openalex.org/W3035060554, https://openalex.org/W2962858109, https://openalex.org/W4281481443, https://openalex.org/W4317933316, https://openalex.org/W2964208960, https://openalex.org/W3196770907, https://openalex.org/W3204166336, https://openalex.org/W2601450892, https://openalex.org/W3091905774, https://openalex.org/W2963656735, https://openalex.org/W2044297386, https://openalex.org/W3013294478, https://openalex.org/W2332757643, https://openalex.org/W2603777577, https://openalex.org/W2171590421, https://openalex.org/W3109684201, https://openalex.org/W2606611007, https://openalex.org/W2963775347 |
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