dc.contributor.author | Prezja, Fabi | |
dc.contributor.author | Äyrämö, Sami | |
dc.contributor.author | Pölönen, Ilkka | |
dc.contributor.author | Ojala, Timo | |
dc.contributor.author | Lahtinen, Suvi | |
dc.contributor.author | Ruusuvuori, Pekka | |
dc.contributor.author | Kuopio, Teijo | |
dc.date.accessioned | 2023-10-03T05:51:30Z | |
dc.date.available | 2023-10-03T05:51:30Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Prezja, F., Äyrämö, S., Pölönen, I., Ojala, T., Lahtinen, S., Ruusuvuori, P., & Kuopio, T. (2023). Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions. <i>Scientific Reports</i>, <i>13</i>, Article 15879. <a href="https://doi.org/10.1038/s41598-023-42357-x" target="_blank">https://doi.org/10.1038/s41598-023-42357-x</a> | |
dc.identifier.other | CONVID_188985036 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/89353 | |
dc.description.abstract | Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests. However, recent work has shown that relevant biomarkers can be extracted from these images using convolutional neural networks (CNNs). The CNN-based biomarkers predicted colorectal cancer patient outcomes comparably to gold standards. Extracting CNN-biomarkers is fast, automatic, and of minimal cost. CNN-based biomarkers rely on the ability of CNNs to recognize distinct tissue types from microscope whole slide images. The quality of these biomarkers (coined ‘Deep Stroma’) depends on the accuracy of CNNs in decomposing all relevant tissue classes. Improving tissue decomposition accuracy is essential for improving the prognostic potential of CNN-biomarkers. In this study, we implemented a novel training strategy to refine an established CNN model, which then surpassed all previous solutions . We obtained a 95.6% average accuracy in the external test set and 99.5% in the internal test set. Our approach reduced errors in biomarker-relevant classes, such as Lymphocytes, and was the first to include interpretability methods. These methods were used to better apprehend our model’s limitations and capabilities. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Nature Publishing Group | |
dc.relation.ispartofseries | Scientific Reports | |
dc.rights | CC BY 4.0 | |
dc.subject.other | colorectal cancer | |
dc.subject.other | machine learning | |
dc.title | Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202310035374 | |
dc.contributor.laitos | Bio- ja ympäristötieteiden laitos | fi |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Department of Biological and Environmental Science | en |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Solu- ja molekyylibiologia | fi |
dc.contributor.oppiaine | Hyvinvoinnin tutkimuksen yhteisö | fi |
dc.contributor.oppiaine | Laskennallinen tiede | fi |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Human and Machine based Intelligence in Learning | fi |
dc.contributor.oppiaine | Computing, Information Technology and Mathematics | fi |
dc.contributor.oppiaine | Cell and Molecular Biology | en |
dc.contributor.oppiaine | School of Wellbeing | en |
dc.contributor.oppiaine | Computational Science | en |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.contributor.oppiaine | Human and Machine based Intelligence in Learning | en |
dc.contributor.oppiaine | Computing, Information Technology and Mathematics | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.relation.issn | 2045-2322 | |
dc.relation.volume | 13 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © The Author(s) 2023 | |
dc.rights.accesslevel | openAccess | fi |
dc.subject.yso | biomarkkerit | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | syöpätaudit | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p12288 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p678 | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.doi | 10.1038/s41598-023-42357-x | |
dc.type.okm | A1 | |