Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da FIOCRUZ (ARCA) |
Texto Completo: | https://www.arca.fiocruz.br/handle/icict/56838 |
Resumo: | Fundação de Apoio à Pesquisa do Estado da Bahia (FAPESB). Inova Fiocruz - Ideias inovadoras. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). |
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Chagas, PauloSouza, LuizPontes, IzabelleCalumby, RodrigoAngelo, MicheleDuarte, AngeloSantos, Washington L. C. dosOliveira, Luciano2023-02-02T16:58:53Z2023-02-02T16:58:53Z2022CHAGAS, Paulo et al. Uncertainty-aware membranous nephropathy classification: A Monte-Carlo dropout approach to detect how certain is the model. Computer Methods in Biomechanics and Biomedical Engineering, p. 1-11, 2022.1476-8259https://www.arca.fiocruz.br/handle/icict/568380.1080/21681163.2022.2029573Fundação de Apoio à Pesquisa do Estado da Bahia (FAPESB). Inova Fiocruz - Ideias inovadoras. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).Universidade Federal Da Bahia. IvisionLab. Salvador, BA, Brasil.Universidade Federal Da Bahia. IvisionLab. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil.Universidade Estadual de Feira de Santana. Departamento de Tecnologia. Salvador, BA, Brasil.Universidade Estadual de Feira de Santana. Departamento de Tecnologia. Salvador, BA, Brasil.Universidade Estadual de Feira de Santana. Departamento de Tecnologia. Salvador, BA, Brasil.Fundação Oswaldo Cruz, Instituto Gonçalo Moniz, Salvador, BA, Brasil.Universidade Federal Da Bahia. IvisionLab. Salvador, BA, Brasil.Membranous nephropathy (MN) is among the most common glomerular diseases that cause nephrotic syndrome in adults. To aid pathologists on performing the MN classification task, we proposed here a pipeline consisted of two steps. Firstly, we assessed four deep-learning-based architectures, namely, ResNet-18, MobileNet, DenseNet, and Wide-ResNet. To achieve more reliable predictions, we adopted and extensively evaluated a Monte-Carlo dropout approach for uncertainty estimation. Using a 10-fold cross-validation setup, all models achieved average F1-scores above 92%, where the highest average value of 93.2% was obtained by using Wide-ResNet. Regarding uncertainty estimation with Wide-ResNet, high uncertainty scores were more associated with erroneous predictions, demonstrating that our approach can assist pathologists in interpreting the predictions with high reliability. We show that uncertainty-based thresholds for decision referral can greatly improve classification performance, increas-ing the accuracy up to 96%. Finally, we investigated how the uncertainty scores relate to complexity scores defined by pathologistsengTaylor and Francis GroupNefropatia membranosaAprendizado profundoEstimativa de incertezaMembranous nephropathyDeep learningUncertainty estimationNefropatiasAprendizado profundoIncertezaUncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the modelinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/56838/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALChagas, Paulo - Uncertainty aware membranous nephropathy classification.pdfChagas, Paulo - Uncertainty aware membranous nephropathy classification.pdfapplication/pdf5862914https://www.arca.fiocruz.br/bitstream/icict/56838/2/Chagas%2c%20Paulo%20-%20Uncertainty%20aware%20membranous%20nephropathy%20classification.pdf7cb049afe4edece8eb9dec156135ff00MD52icict/568382023-03-15 14:32:51.581oai:www.arca.fiocruz.br:icict/56838Q0VTU8ODTyBOw4NPIEVYQ0xVU0lWQSBERSBESVJFSVRPUyBBVVRPUkFJUwoKQW8gYWNlaXRhciBvcyBURVJNT1MgZSBDT05EScOHw5VFUyBkZXN0YSBDRVNTw4NPLCBvIEFVVE9SIGUvb3UgVElUVUxBUiBkZSBkaXJlaXRvcwphdXRvcmFpcyBzb2JyZSBhIE9CUkEgZGUgcXVlIHRyYXRhIGVzdGUgZG9jdW1lbnRvOgoKKDEpIENFREUgZSBUUkFOU0ZFUkUsIHRvdGFsIGUgZ3JhdHVpdGFtZW50ZSwgw6AgRklPQ1JVWiAtIEZVTkRBw4fDg08gT1NXQUxETyBDUlVaLCBlbQpjYXLDoXRlciBwZXJtYW5lbnRlLCBpcnJldm9nw6F2ZWwgZSBOw4NPIEVYQ0xVU0lWTywgdG9kb3Mgb3MgZGlyZWl0b3MgcGF0cmltb25pYWlzIE7Dg08KQ09NRVJDSUFJUyBkZSB1dGlsaXphw6fDo28gZGEgT0JSQSBhcnTDrXN0aWNhIGUvb3UgY2llbnTDrWZpY2EgaW5kaWNhZGEgYWNpbWEsIGluY2x1c2l2ZSBvcyBkaXJlaXRvcwpkZSB2b3ogZSBpbWFnZW0gdmluY3VsYWRvcyDDoCBPQlJBLCBkdXJhbnRlIHRvZG8gbyBwcmF6byBkZSBkdXJhw6fDo28gZG9zIGRpcmVpdG9zIGF1dG9yYWlzLCBlbQpxdWFscXVlciBpZGlvbWEgZSBlbSB0b2RvcyBvcyBwYcOtc2VzOwoKKDIpIEFDRUlUQSBxdWUgYSBjZXNzw6NvIHRvdGFsIG7Do28gZXhjbHVzaXZhLCBwZXJtYW5lbnRlIGUgaXJyZXZvZ8OhdmVsIGRvcyBkaXJlaXRvcyBhdXRvcmFpcwpwYXRyaW1vbmlhaXMgbsOjbyBjb21lcmNpYWlzIGRlIHV0aWxpemHDp8OjbyBkZSBxdWUgdHJhdGEgZXN0ZSBkb2N1bWVudG8gaW5jbHVpLCBleGVtcGxpZmljYXRpdmFtZW50ZSwKb3MgZGlyZWl0b3MgZGUgZGlzcG9uaWJpbGl6YcOnw6NvIGUgY29tdW5pY2HDp8OjbyBww7pibGljYSBkYSBPQlJBLCBlbSBxdWFscXVlciBtZWlvIG91IHZlw61jdWxvLAppbmNsdXNpdmUgZW0gUmVwb3NpdMOzcmlvcyBEaWdpdGFpcywgYmVtIGNvbW8gb3MgZGlyZWl0b3MgZGUgcmVwcm9kdcOnw6NvLCBleGliacOnw6NvLCBleGVjdcOnw6NvLApkZWNsYW1hw6fDo28sIHJlY2l0YcOnw6NvLCBleHBvc2nDp8OjbywgYXJxdWl2YW1lbnRvLCBpbmNsdXPDo28gZW0gYmFuY28gZGUgZGFkb3MsIHByZXNlcnZhw6fDo28sIGRpZnVzw6NvLApkaXN0cmlidWnDp8OjbywgZGl2dWxnYcOnw6NvLCBlbXByw6lzdGltbywgdHJhZHXDp8OjbywgZHVibGFnZW0sIGxlZ2VuZGFnZW0sIGluY2x1c8OjbyBlbSBub3ZhcyBvYnJhcyBvdQpjb2xldMOibmVhcywgcmV1dGlsaXphw6fDo28sIGVkacOnw6NvLCBwcm9kdcOnw6NvIGRlIG1hdGVyaWFsIGRpZMOhdGljbyBlIGN1cnNvcyBvdSBxdWFscXVlciBmb3JtYSBkZQp1dGlsaXphw6fDo28gbsOjbyBjb21lcmNpYWw7CgooMykgUkVDT05IRUNFIHF1ZSBhIGNlc3PDo28gYXF1aSBlc3BlY2lmaWNhZGEgY29uY2VkZSDDoCBGSU9DUlVaIC0gRlVOREHDh8ODTyBPU1dBTERPCkNSVVogbyBkaXJlaXRvIGRlIGF1dG9yaXphciBxdWFscXVlciBwZXNzb2Eg4oCTIGbDrXNpY2Egb3UganVyw61kaWNhLCBww7pibGljYSBvdSBwcml2YWRhLCBuYWNpb25hbCBvdQplc3RyYW5nZWlyYSDigJMgYSBhY2Vzc2FyIGUgdXRpbGl6YXIgYW1wbGFtZW50ZSBhIE9CUkEsIHNlbSBleGNsdXNpdmlkYWRlLCBwYXJhIHF1YWlzcXVlcgpmaW5hbGlkYWRlcyBuw6NvIGNvbWVyY2lhaXM7CgooNCkgREVDTEFSQSBxdWUgYSBvYnJhIMOpIGNyaWHDp8OjbyBvcmlnaW5hbCBlIHF1ZSDDqSBvIHRpdHVsYXIgZG9zIGRpcmVpdG9zIGFxdWkgY2VkaWRvcyBlIGF1dG9yaXphZG9zLApyZXNwb25zYWJpbGl6YW5kby1zZSBpbnRlZ3JhbG1lbnRlIHBlbG8gY29udGXDumRvIGUgb3V0cm9zIGVsZW1lbnRvcyBxdWUgZmF6ZW0gcGFydGUgZGEgT0JSQSwKaW5jbHVzaXZlIG9zIGRpcmVpdG9zIGRlIHZveiBlIGltYWdlbSB2aW5jdWxhZG9zIMOgIE9CUkEsIG9icmlnYW5kby1zZSBhIGluZGVuaXphciB0ZXJjZWlyb3MgcG9yCmRhbm9zLCBiZW0gY29tbyBpbmRlbml6YXIgZSByZXNzYXJjaXIgYSBGSU9DUlVaIC0gRlVOREHDh8ODTyBPU1dBTERPIENSVVogZGUKZXZlbnR1YWlzIGRlc3Blc2FzIHF1ZSB2aWVyZW0gYSBzdXBvcnRhciwgZW0gcmF6w6NvIGRlIHF1YWxxdWVyIG9mZW5zYSBhIGRpcmVpdG9zIGF1dG9yYWlzIG91CmRpcmVpdG9zIGRlIHZveiBvdSBpbWFnZW0sIHByaW5jaXBhbG1lbnRlIG5vIHF1ZSBkaXogcmVzcGVpdG8gYSBwbMOhZ2lvIGUgdmlvbGHDp8O1ZXMgZGUgZGlyZWl0b3M7CgooNSkgQUZJUk1BIHF1ZSBjb25oZWNlIGEgUG9sw610aWNhIEluc3RpdHVjaW9uYWwgZGUgQWNlc3NvIEFiZXJ0byBkYSBGSU9DUlVaIC0gRlVOREHDh8ODTwpPU1dBTERPIENSVVogZSBhcyBkaXJldHJpemVzIHBhcmEgbyBmdW5jaW9uYW1lbnRvIGRvIHJlcG9zaXTDs3JpbyBpbnN0aXR1Y2lvbmFsIEFSQ0EuCgpBIFBvbMOtdGljYSBJbnN0aXR1Y2lvbmFsIGRlIEFjZXNzbyBBYmVydG8gZGEgRklPQ1JVWiAtIEZVTkRBw4fDg08gT1NXQUxETyBDUlVaIHJlc2VydmEKZXhjbHVzaXZhbWVudGUgYW8gQVVUT1Igb3MgZGlyZWl0b3MgbW9yYWlzIGUgb3MgdXNvcyBjb21lcmNpYWlzIHNvYnJlIGFzIG9icmFzIGRlIHN1YSBhdXRvcmlhCmUvb3UgdGl0dWxhcmlkYWRlLCBzZW5kbyBvcyB0ZXJjZWlyb3MgdXN1w6FyaW9zIHJlc3BvbnPDoXZlaXMgcGVsYSBhdHJpYnVpw6fDo28gZGUgYXV0b3JpYSBlIG1hbnV0ZW7Dp8OjbwpkYSBpbnRlZ3JpZGFkZSBkYSBPQlJBIGVtIHF1YWxxdWVyIHV0aWxpemHDp8Ojby4KCkEgUG9sw610aWNhIEluc3RpdHVjaW9uYWwgZGUgQWNlc3NvIEFiZXJ0byBkYSBGSU9DUlVaIC0gRlVOREHDh8ODTyBPU1dBTERPIENSVVoKcmVzcGVpdGEgb3MgY29udHJhdG9zIGUgYWNvcmRvcyBwcmVleGlzdGVudGVzIGRvcyBBdXRvcmVzIGNvbSB0ZXJjZWlyb3MsIGNhYmVuZG8gYW9zIEF1dG9yZXMKaW5mb3JtYXIgw6AgSW5zdGl0dWnDp8OjbyBhcyBjb25kacOnw7VlcyBlIG91dHJhcyByZXN0cmnDp8O1ZXMgaW1wb3N0YXMgcG9yIGVzdGVzIGluc3RydW1lbnRvcy4KRepositório 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dc.title.en_US.fl_str_mv |
Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model |
title |
Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model |
spellingShingle |
Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model Chagas, Paulo Nefropatia membranosa Aprendizado profundo Estimativa de incerteza Membranous nephropathy Deep learning Uncertainty estimation Nefropatias Aprendizado profundo Incerteza |
title_short |
Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model |
title_full |
Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model |
title_fullStr |
Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model |
title_full_unstemmed |
Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model |
title_sort |
Uncertainty-aware membranous nephropathy classification: a Monte-Carlo dropout approach to detect how certain is the model |
author |
Chagas, Paulo |
author_facet |
Chagas, Paulo Souza, Luiz Pontes, Izabelle Calumby, Rodrigo Angelo, Michele Duarte, Angelo Santos, Washington L. C. dos Oliveira, Luciano |
author_role |
author |
author2 |
Souza, Luiz Pontes, Izabelle Calumby, Rodrigo Angelo, Michele Duarte, Angelo Santos, Washington L. C. dos Oliveira, Luciano |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Chagas, Paulo Souza, Luiz Pontes, Izabelle Calumby, Rodrigo Angelo, Michele Duarte, Angelo Santos, Washington L. C. dos Oliveira, Luciano |
dc.subject.other.en_US.fl_str_mv |
Nefropatia membranosa Aprendizado profundo Estimativa de incerteza |
topic |
Nefropatia membranosa Aprendizado profundo Estimativa de incerteza Membranous nephropathy Deep learning Uncertainty estimation Nefropatias Aprendizado profundo Incerteza |
dc.subject.en.en_US.fl_str_mv |
Membranous nephropathy Deep learning Uncertainty estimation |
dc.subject.decs.en_US.fl_str_mv |
Nefropatias Aprendizado profundo Incerteza |
description |
Fundação de Apoio à Pesquisa do Estado da Bahia (FAPESB). Inova Fiocruz - Ideias inovadoras. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022 |
dc.date.accessioned.fl_str_mv |
2023-02-02T16:58:53Z |
dc.date.available.fl_str_mv |
2023-02-02T16:58:53Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
CHAGAS, Paulo et al. Uncertainty-aware membranous nephropathy classification: A Monte-Carlo dropout approach to detect how certain is the model. Computer Methods in Biomechanics and Biomedical Engineering, p. 1-11, 2022. |
dc.identifier.uri.fl_str_mv |
https://www.arca.fiocruz.br/handle/icict/56838 |
dc.identifier.issn.en_US.fl_str_mv |
1476-8259 |
dc.identifier.doi.none.fl_str_mv |
0.1080/21681163.2022.2029573 |
identifier_str_mv |
CHAGAS, Paulo et al. Uncertainty-aware membranous nephropathy classification: A Monte-Carlo dropout approach to detect how certain is the model. Computer Methods in Biomechanics and Biomedical Engineering, p. 1-11, 2022. 1476-8259 0.1080/21681163.2022.2029573 |
url |
https://www.arca.fiocruz.br/handle/icict/56838 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Taylor and Francis Group |
publisher.none.fl_str_mv |
Taylor and Francis Group |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da FIOCRUZ (ARCA) instname:Fundação Oswaldo Cruz (FIOCRUZ) instacron:FIOCRUZ |
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Fundação Oswaldo Cruz (FIOCRUZ) |
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FIOCRUZ |
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FIOCRUZ |
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Repositório Institucional da FIOCRUZ (ARCA) |
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Repositório Institucional da FIOCRUZ (ARCA) |
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Repositório Institucional da FIOCRUZ (ARCA) - Fundação Oswaldo Cruz (FIOCRUZ) |
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repositorio.arca@fiocruz.br |
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