González García, Xabier

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https://academica-e.unavarra.es/handle/2454/49178

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González García

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Xabier

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Estadística, Informática y Matemáticas

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1173318

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812829

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2022 - 20242
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    PublicationOpen Access
    A rule-based approach for interpretable intensity-modulated radiation therapy treatment selection
    (IEEE, 2024-08-05) González García, Xabier; Fumanal Idocin, Javier; Nunez do Rio, Joan M.; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    Artificial Intelligence (AI) methods are becoming essential in healthcare. In the context of Intensity-Modulated Radiation Therapy (IMRT), Knowledge-Based Planning (KBP) methodologies have enabled the modification of treatments in real-time to accommodate morphological changes in patients. KBP for IMRT is a data-driven approach that utilises real-time medical imaging to adjust the radiation dose for a patient as needed for the different stages of an illness. In this work we present an interpretable AI model that selects the best IMRT treatment alternatives and determines which is the best. We use an Adaptive Neuforuzzy Adaptive Inference System (ANFIS), which combines the potential of a neural network with the interpretability of a rule based system. We train the model in a supervised manner using the OpenKBP challenge data repository. For this purpose, we also developed a data augmentation method that is supported by Diffusion Probabilistic Models. This approach enables the generation of a wider spectrum of treatment qualities and aids regularisation. The primary advantage of this framework resides in its ability to offer explanations, which is essential in the deployment of medical procedures in real life. Moreover, it serves as a valuable means to test hypotheses concerning the quality of IMRT treatments. Our study reveals that the developed tool has substantial potential to establish itself as a reference in the realm of explainable IMRT treatment selection tools.
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    PublicationOpen Access
    Aplicación del estado del arte del aprendizaje profundo en las interfaces cerebro-máquina
    (2022) González García, Xabier; Fumanal Idocin, Javier; Fernández Fernández, Francisco Javier; Escuela Técnica Superior de Ingeniería Agronómica y Biociencias; Nekazaritzako Ingeniaritzako eta Biozientzietako Goi Mailako Eskola Teknikoa
    Los avances tecnológicos han facilitado la tarea de captar actividad bioeléctrica cerebral mediante dispositivos electrónicos. Las interfaces cerebro-máquina son las encargadas de integrar la actividad captada con ordenadores para su posterior análisis. No obstante, obtener datos de calidad partir de las señales cerebrales noes una tarea sencilla. Diversas técnicas han sido estudiados durante décadas en la comunidad científica, dentro de las cuales destacan, por su rendimiento y resultados prometedores, los modelos de aprendizaje automático. El presente trabajo tiene como objetivo implementar y experimentar con técnicas de aprendizaje profundo del estado del arte para así descifrar la información que traen este tipo de señales, haciendo uso de la Tecnología de Interfaz cerebro-máquina.