Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling
Pathology diagnosis of colorectal cancer is time-consuming and requires a high level of expertise. However, it is an essential step towards establishing the adequate treatment. The need to analyse a large number of these histopathological images calls for automatic tools capable of aiding pathologis...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/224338 |
| Acceso en línea: | https://hdl.handle.net/2445/224338 |
| Access Level: | acceso abierto |
| Palabra clave: | Histologia Aprenentatge profund Càncer colorectal Histology Deep learning (Machine learning) Colorectal cancer |
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Efficient deep neural networks for cancer detection on histopathology combining attention and image downsamplingSocolovsky, MiguelLópez, AlbertoGreenson, Joel K.Rennert, Gad Gruber, Stephen B.Moreno Aguado, VíctorHistologiaAprenentatge profundCàncer colorectalHistologyDeep learning (Machine learning)Colorectal cancerPathology diagnosis of colorectal cancer is time-consuming and requires a high level of expertise. However, it is an essential step towards establishing the adequate treatment. The need to analyse a large number of these histopathological images calls for automatic tools capable of aiding pathologists in this arduous task. Deep learning techniques, together with the wealth of data available nowadays, provide a promising candidate for such job. Adopting state-of-the-art artificial intelligence algorithms, we developed a model to accurately detect colorectal cancer in digitalised histopathological whole-slide images. Our end-to-end approach uses the principles of multiple-instance learning combined with deep convolutional neural networks in order to fully leverage the information contained within each image and make robust predictions at the patient's level. The model also allows to highlight the areas in the slide most likely to harbour tumour tissue. Given the finite computational resources available, working at maximum resolution can be detrimental. Therefore, we explored the impact of lowering the working image resolution. The algorithms were trained and validated on a subset of more than 1300 patients of the Molecular Epidemiology of Colorectal Cancer study with histopathology images available. These images gave rise to > 10(5) tiles of 256 x 256 pixels each. Once we identified the best-performing model we put it to the test on images from The Cancer Genome Atlas. We obtained the best outcomes working at 4 mu m/pix, achieving the following metrics on the test dataset: F1-Score of 0.96, a Matthews correlation coefficient of 0.92 and an area under the receiver operating characteristic curve of 0.99. These results are exceptional and prove that computational costs can be reduced while keeping the performance up to standard.Springer Science and Business Media LLC2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/224338Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1038/s41598-025-20954-2Scientific Reports, 2025, vol. 15, 36917https://doi.org/10.1038/s41598-025-20954-2cc-by (c) Socolovsky, Miguel et al., 2025https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2243382026-05-27T06:46:51Z |
| dc.title.none.fl_str_mv |
Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling |
| title |
Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling |
| spellingShingle |
Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling Socolovsky, Miguel Histologia Aprenentatge profund Càncer colorectal Histology Deep learning (Machine learning) Colorectal cancer |
| title_short |
Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling |
| title_full |
Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling |
| title_fullStr |
Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling |
| title_full_unstemmed |
Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling |
| title_sort |
Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling |
| dc.creator.none.fl_str_mv |
Socolovsky, Miguel López, Alberto Greenson, Joel K. Rennert, Gad Gruber, Stephen B. Moreno Aguado, Víctor |
| author |
Socolovsky, Miguel |
| author_facet |
Socolovsky, Miguel López, Alberto Greenson, Joel K. Rennert, Gad Gruber, Stephen B. Moreno Aguado, Víctor |
| author_role |
author |
| author2 |
López, Alberto Greenson, Joel K. Rennert, Gad Gruber, Stephen B. Moreno Aguado, Víctor |
| author2_role |
author author author author author |
| dc.subject.none.fl_str_mv |
Histologia Aprenentatge profund Càncer colorectal Histology Deep learning (Machine learning) Colorectal cancer |
| topic |
Histologia Aprenentatge profund Càncer colorectal Histology Deep learning (Machine learning) Colorectal cancer |
| description |
Pathology diagnosis of colorectal cancer is time-consuming and requires a high level of expertise. However, it is an essential step towards establishing the adequate treatment. The need to analyse a large number of these histopathological images calls for automatic tools capable of aiding pathologists in this arduous task. Deep learning techniques, together with the wealth of data available nowadays, provide a promising candidate for such job. Adopting state-of-the-art artificial intelligence algorithms, we developed a model to accurately detect colorectal cancer in digitalised histopathological whole-slide images. Our end-to-end approach uses the principles of multiple-instance learning combined with deep convolutional neural networks in order to fully leverage the information contained within each image and make robust predictions at the patient's level. The model also allows to highlight the areas in the slide most likely to harbour tumour tissue. Given the finite computational resources available, working at maximum resolution can be detrimental. Therefore, we explored the impact of lowering the working image resolution. The algorithms were trained and validated on a subset of more than 1300 patients of the Molecular Epidemiology of Colorectal Cancer study with histopathology images available. These images gave rise to > 10(5) tiles of 256 x 256 pixels each. Once we identified the best-performing model we put it to the test on images from The Cancer Genome Atlas. We obtained the best outcomes working at 4 mu m/pix, achieving the following metrics on the test dataset: F1-Score of 0.96, a Matthews correlation coefficient of 0.92 and an area under the receiver operating characteristic curve of 0.99. These results are exceptional and prove that computational costs can be reduced while keeping the performance up to standard. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2445/224338 |
| url |
https://hdl.handle.net/2445/224338 |
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Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Reproducció del document publicat a: https://doi.org/10.1038/s41598-025-20954-2 Scientific Reports, 2025, vol. 15, 36917 https://doi.org/10.1038/s41598-025-20954-2 |
| dc.rights.none.fl_str_mv |
cc-by (c) Socolovsky, Miguel et al., 2025 https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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cc-by (c) Socolovsky, Miguel et al., 2025 https://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf |
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Springer Science and Business Media LLC |
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Springer Science and Business Media LLC |
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Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) reponame:Dipòsit Digital de la UB instname:Universidad de Barcelona |
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Universidad de Barcelona |
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Dipòsit Digital de la UB |
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Dipòsit Digital de la UB |
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