@phdthesis{10902/38196,
year = {2025},
month = {11},
url = {https://hdl.handle.net/10902/38196},
abstract = {La adopción clínica de la inteligencia artificial está limitada por problemas relacionados con los datos, los algoritmos y la falta de validación clínica rigurosa. Esta tesis se centra en mejorar los sistemas de diagnóstico basados en tomografía computarizada con técnicas de aprendizaje profundo explicables y robustas. En la primera parte se presentan los fundamentos del aprendizaje profundo, los principios físicos de las imágenes médicas, junto con las limitaciones actuales (estandarización, interoperabilidad, reproducibilidad). La segunda parte profundiza en dos aplicaciones concretas de aprendizaje profundo, el pronóstico de hemorragias intracraneales y el diagnóstico precoz del cáncer de pulmón. La inteligencia artificial en salud depende de colaboraciones interdisciplinarias y validaciones externas de alta calidad para identificar necesidades clínicas reales, avanzar en la medicina personalizada, además de garantizar una traslación clínica segura y fiable.},
abstract = {The clinical adoption of artificial intelligence is hindered by challenges related to the data, the algorithms, and lack of rigorous clinical validation. This thesis is focused on enhancing computed tomography-based diagnostic systems with robust and explainable deep learning techniques. The first part presents the foundations of deep learning, the fundamentals of physics in medical imaging, together with current limitations to the clinical adoption of artificial intelligence (standardization, interoperability, reproducibility). The second part delves into two specific applications of deep learning, intracranial hemorrhage prognosis and lung cancer early diagnosis. Artificial intelligence in healthcare relies on interdisciplinary collaborations and high quality external validation to identify real clinical needs, advance personalized medicine, in addition to ensuring safe and reliable clinical translation.},
organization = {This work was funded by the Ministry of Education of Spain through the FPU (Training programme for Academic Staff) grant with reference FPU21-04458, and the FPU grant for research stays EST24/00533, as well as the project AI4EOSC “Artificial Intelligence for the European Open Science Cloud”, that has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101058593. Funding for the collaboration with CIMA-CUN has also been allocated from the Lung Ambition Alliance Prize for Research on Lung Cancer Early Detection (01/11/2021-01/11/2024).},
title = {Aprendizaje profundo explicable y robusto para sistemas de diagnóstico basados en tomografía computarizada},
author = {Cobo Cano, Miriam},
}