Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach
Use este enlace para citar
http://hdl.handle.net/2183/34758Coleccións
- GI-LIDIA - Artigos [54]
Metadatos
Mostrar o rexistro completo do ítemTítulo
Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approachAutor(es)
Data
2023-11Cita bibliográfica
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
Resumo
[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.
Palabras chave
Human-in-the-loop machine learning
Active learning
Interactive machine learning
Pancreatic cancer
Generative adversarial network
Active learning
Interactive machine learning
Pancreatic cancer
Generative adversarial network
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
0941-0643
Ítems relacionados
Mostrando ítems relacionados por Título, autor ou materia.
-
Una integración a sistemas de gestión de aprendizaje en estándares de un sistema barra-bola
Montoro, Alicia; Ruano Ruano, Ildefonso; Estévez, Elisabet; Gómez Ortega, Juan; Gámez García, Javier (Universidade da Coruña, Servizo de Publicacións, 2021)[Resumen] Los laboratorios de tipo online tienen cada vez más aceptación dentro de la educación universitaria relacionada con las ciencias, tecnologías, ingenierías y matemáticas (CTIM o STEM en inglés), donde el trabajo ... -
Análisis de un robot abierto de bajo coste para docencia de aprendizaje automático
Bes Carreras, Jorge; García-Barcos, Javier; Martínez-Cantín, Rubén (Universidade da Coruña. Servizo de Publicacións, 2023)[Resumen] En este trabajo estudiamos el comportamiento, ventajas e inconvenientes del robot Trifinger para la docencia de estudiantes de ingeniería y automática. El robot Trifinger es un robot de diseño abierto a nivel ... -
Lifelong Learning and Personal Learning Environments: a Productive Symbiosis in Higher Education
García-Martínez, José Antonio; González-Sanmamed, Mercedes; Muñoz-Carril, Pablo-César (Universidad Complutense de Madrid, 2023-01-09)[Abstract] The aim of this study was to analyze lifelong learning (LLL) and its relationship with the educational approach of the personal learning environment (PLE). In the last year students do various degrees at a ...