Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex o...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/72830 |
| Acceso en línea: | https://hdl.handle.net/10230/72830 http://dx.doi.org/10.1038/s41598-024-52063-x |
| Access Level: | acceso abierto |
| Palabra clave: | Colonoscòpia Enginyeria biomèdica Aprenentatge profund (Aprenentatge automàtic) |
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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challengeAli, SharibGaldran, AdrianGonzález Ballester, Miguel Ángel, 1973-East, James E.ColonoscòpiaEnginyeria biomèdicaAprenentatge profund (Aprenentatge automàtic)Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.Nature Research2026202620242026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/72830http://dx.doi.org/10.1038/s41598-024-52063-xreponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésScientific Reports. 2024 Jan 23;14(1):2032This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/728302026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge |
| title |
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge |
| spellingShingle |
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge Ali, Sharib Colonoscòpia Enginyeria biomèdica Aprenentatge profund (Aprenentatge automàtic) |
| title_short |
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge |
| title_full |
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge |
| title_fullStr |
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge |
| title_full_unstemmed |
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge |
| title_sort |
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge |
| dc.creator.none.fl_str_mv |
Ali, Sharib Galdran, Adrian González Ballester, Miguel Ángel, 1973- East, James E. |
| author |
Ali, Sharib |
| author_facet |
Ali, Sharib Galdran, Adrian González Ballester, Miguel Ángel, 1973- East, James E. |
| author_role |
author |
| author2 |
Galdran, Adrian González Ballester, Miguel Ángel, 1973- East, James E. |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Colonoscòpia Enginyeria biomèdica Aprenentatge profund (Aprenentatge automàtic) |
| topic |
Colonoscòpia Enginyeria biomèdica Aprenentatge profund (Aprenentatge automàtic) |
| description |
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures. |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2024 2026 2026 2026 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://hdl.handle.net/10230/72830 http://dx.doi.org/10.1038/s41598-024-52063-x |
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https://hdl.handle.net/10230/72830 http://dx.doi.org/10.1038/s41598-024-52063-x |
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Inglés |
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Inglés |
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Scientific Reports. 2024 Jan 23;14(1):2032 |
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http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf application/pdf |
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Nature Research |
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Nature Research |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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