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Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach
dc.contributor.author | Mosqueira-Rey, E. | |
dc.contributor.author | Hernández-Pereira, Elena | |
dc.contributor.author | Bobes-Bascarán, José | |
dc.contributor.author | Alonso Ríos, David | |
dc.contributor.author | Pérez-Sánchez, Alberto | |
dc.contributor.author | Fernández-Leal, Ángel | |
dc.contributor.author | Moret-Bonillo, Vicente | |
dc.contributor.author | Vidal-Ínsua, Yolanda | |
dc.contributor.author | Vázquez-Rivera, Francisca | |
dc.date.accessioned | 2024-01-08T12:36:25Z | |
dc.date.available | 2024-01-08T12:36:25Z | |
dc.date.issued | 2023-11 | |
dc.identifier.citation | Mosqueira-Rey, E., Hernández-Pereira, E., Bobes-Bascarán, J. et al. Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach. Neural Comput & Applic (2023). https://doi.org/10.1007/s00521-023-09197-2 | es_ES |
dc.identifier.issn | 0941-0643 | |
dc.identifier.uri | http://hdl.handle.net/2183/34758 | |
dc.description.abstract | [Abstract]: Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges. | es_ES |
dc.description.sponsorship | This work has been supported by the State Research Agency of the Spanish Government (Grant PID2019-107194GB-I00/AEI/10.13039/501100011033) and by the Xunta de Galicia (Grant ED431C 2022/44), supported in turn by the EU European Regional Development Fund. We wish to acknowledge support received from the Centro de Investigación de Galicia CITIC, funded by the Xunta de Galicia and the European Regional Development Fund (Galicia 2014–2020 Program; Grant ED431G 2019/01). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/44 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer Nature | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107194GB-I00/ES/ANÁLISIS DE ESTRATEGIAS PARA INCORPORAR HUMANOS AL PROCESO DE APRENDIZAJE AUTOMÁTICO Y SU APLICACIÓN A LA INVESTIGACIÓN DEL CÁNCER PANCREÁTICO | es_ES |
dc.relation.uri | https://doi.org/10.1007/s00521-023-09197-2 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Human-in-the-loop machine learning | es_ES |
dc.subject | Active learning | es_ES |
dc.subject | Interactive machine learning | es_ES |
dc.subject | Pancreatic cancer | es_ES |
dc.subject | Generative adversarial network | es_ES |
dc.title | Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Neural Computing and Applications | es_ES |
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