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dc.contributor.author | Silva-Rodríguez, Julio![]() |
es_ES |
dc.contributor.author | Schmidt, Arne![]() |
es_ES |
dc.contributor.author | Sales, María A.![]() |
es_ES |
dc.contributor.author | Molina, Rafael![]() |
es_ES |
dc.contributor.author | Naranjo Ornedo, Valeriana![]() |
es_ES |
dc.date.accessioned | 2023-07-27T18:01:39Z | |
dc.date.available | 2023-07-27T18:01:39Z | |
dc.date.issued | 2022-08 | es_ES |
dc.identifier.issn | 0010-4825 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/195665 | |
dc.description.abstract | [EN] Multiple instance learning (MIL) deals with data grouped into bags of instances, of which only the global information is known. In recent years, this weakly supervised learning paradigm has become very popular in histological image analysis because it alleviates the burden of labeling all cancerous regions of large Whole Slide Images (WSIs) in detail. However, these methods require large datasets to perform properly, and many approaches only focus on simple binary classification. This often does not match the real-world problems where multi-label settings are frequent and possible constraints must be taken into account. In this work, we propose a novel multi-label MIL formulation based on inequality constraints that is able to incorporate prior knowledge about instance proportions. Our method has a theoretical foundation in optimization with log-barrier extensions, applied to bag-level class proportions. This encourages the model to respect the proportion ordering during training. Extensive experiments on a new public dataset of prostate cancer WSIs analysis, SICAP-MIL, demonstrate that using the prior proportion information we can achieve instance-level results similar to supervised methods on datasets of similar size. In comparison with prior MIL settings, our method allows for similar to 13% improvements in instance-level accuracy, and similar to 3% in the multi-label mean area under the ROC curve at the bag-level. | es_ES |
dc.description.sponsorship | This work was supported by the Spanish Ministry of Economy and Competitiveness through project PID2019-105142RB-C2. The work of A. Schmidt was funded from the European Union's Horizon 2020 research and innovation programme under the Marie SkAodowska Curie grant agreement No 860627 (CLARIFY Project). The hardware NVIDIA DGXA100 used in the experimental part of this work was funded by Generalitat Valenciana/European Union through the European Regional Development Fund (ERDF) of the Valencian Community (IDIFEDER/2020/030). Funding for open access charge: Universitat Politecnica de Valencia. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Computers in Biology and Medicine | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Multiple instance learning | es_ES |
dc.subject | Histology | es_ES |
dc.subject | Proportion | es_ES |
dc.subject | Inequality constraints | es_ES |
dc.subject | Extended log-barrier | es_ES |
dc.subject.classification | TEORÍA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | Proportion constrained weakly supervised histopathology image classification | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.compbiomed.2022.105714 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105142RB-C21/ES/CARACTERIZACION DE NEOPLASIAS DE CELULAS FUSIFORMES EN IMAGENES HISTOLOGICAS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//IDIFEDER%2F2020%2F030/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/860627/EU | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació | es_ES |
dc.description.bibliographicCitation | Silva-Rodríguez, J.; Schmidt, A.; Sales, MA.; Molina, R.; Naranjo Ornedo, V. (2022). Proportion constrained weakly supervised histopathology image classification. Computers in Biology and Medicine. 147:1-9. https://doi.org/10.1016/j.compbiomed.2022.105714 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.compbiomed.2022.105714 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 9 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 147 | es_ES |
dc.identifier.pmid | 35753089 | es_ES |
dc.relation.pasarela | S\468199 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
upv.costeAPC | 2432,1 | es_ES |