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Learning from crowds in digital pathology using scalable variational Gaussian processes
dc.contributor.author | López Pérez, Miguel | |
dc.contributor.author | Molina Soriano, Rafael | |
dc.date.accessioned | 2021-09-23T10:26:13Z | |
dc.date.available | 2021-09-23T10:26:13Z | |
dc.date.issued | 2021-06-02 | |
dc.identifier.citation | López-Pérez, M... [et al.]. Learning from crowds in digital pathology using scalable variational Gaussian processes. Sci Rep 11, 11612 (2021). [https://doi.org/10.1038/s41598-021-90821-3] | es_ES |
dc.identifier.uri | http://hdl.handle.net/10481/70399 | |
dc.description | This work was supported by the Agencia Estatal de Investigacion of the Spanish Ministerio de Ciencia e Innovacion under contract PID2019-105142RB-C22/AEI/10.13039/501100011033, and the United States National Institutes of Health National Cancer Institute Grants U01CA220401 and U24CA19436201. P.M. contribution was mostly before joining Microsoft Research, when he was supported by La Caixa Banking Foundation (ID 100010434, Barcelona, Spain) through La Caixa Fellowship for Doctoral Studies LCF/BQ/ES17/11600011. | es_ES |
dc.description.abstract | The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy crowd-sourced labels. The need to scale labeling is acute but particularly challenging in medical applications like pathology, due to the expertise required to generate quality labels and the limited availability of qualified experts. In this paper we investigate the application of Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR) in digital pathology. We compare SVGPCR with other crowdsourcing methods using a large multi-rater dataset where pathologists, pathology residents, and medical students annotated tissue regions breast cancer. Our study shows that SVGPCR is competitive with equivalent methods trained using goldstandard pathologist generated labels, and that SVGPCR meets or exceeds the performance of other crowdsourcing methods based on deep learning. We also show how SVGPCR can effectively learn the class-conditional reliabilities of individual annotators and demonstrate that Gaussian-process classifiers have comparable performance to similar deep learning methods. These results suggest that SVGPCR can meaningfully engage non-experts in pathology labeling tasks, and that the classconditional reliabilities estimated by SVGPCR may assist in matching annotators to tasks where they perform well. | es_ES |
dc.description.sponsorship | Agencia Estatal de Investigacion of the Spanish Ministerio de Ciencia e Innovacion PID2019-105142RB-C22/AEI/10.13039/501100011033 | es_ES |
dc.description.sponsorship | United States Department of Health & Human Services | es_ES |
dc.description.sponsorship | National Institutes of Health (NIH) - USA | es_ES |
dc.description.sponsorship | NIH National Cancer Institute (NCI) U01CA220401 U24CA19436201 | es_ES |
dc.description.sponsorship | La Caixa Banking Foundation (Barcelona, Spain) Barcelona, Spain) through La Caixa Fellowship 100010434 LCF/BQ/ES17/11600011 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Nature | es_ES |
dc.rights | Atribución 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.title | Learning from crowds in digital pathology using scalable variational Gaussian processes | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.1038/s41598-021-90821-3 | |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |