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https://hdl.handle.net/1822/71390
Título: | Combining image and non-image clinical data: An infrastructure that allows machine learning studies in a hospital environment |
Autor(es): | Espanha, Raphael Thiele, Frank Shakirin, Georgy Roggenfelder, Jens Zeiter, Sascha Stavrinou, Pantelis Alves, Victor Perkuhn, Michael |
Palavras-chave: | Brain tumor segmentation Clinical environment Convolution neural networks Deep learning Docker Machine learning workflow Medical image research XNAT |
Data: | 2019 |
Editora: | Springer Verlag |
Revista: | Advances in Intelligent Systems and Computing |
Citação: | Espanha R. et al. (2019) Combining Image and Non-image Clinical Data: An Infrastructure that Allows Machine Learning Studies in a Hospital Environment. In: De La Prieta F., Omatu S., Fernández-Caballero A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_39 |
Resumo(s): | Over the past years Machine Learning and Deep Learning techniques are showing their huge potential in medical research. However, this research is mainly done by using public or private datasets that were created for study purposes. Despite ensuring reproducibility, these datasets need to be constantly updated. In this paper we present an infrastructure that transfers, processes and stores medical image and non-image data in an organized and secure workflow. This infrastructure concept has been tested at a university hospital. XNAT, an extensible open-source imaging informatics software platform was extended to store the non-image data and later feed the Machine Learning models. The resulting infrastructure allowed an easy implementation of a Deep Learning approach for brain tumor segmentation with potential for other medical image research scenarios. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/71390 |
ISBN: | 978-3-319-94648-1 |
e-ISBN: | 978-3-319-94649-8 |
DOI: | 10.1007/978-3-319-94649-8_39 |
ISSN: | 2194-5357 |
Versão da editora: | https://link.springer.com/chapter/10.1007%2F978-3-319-94649-8_39 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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17.pdf Acesso restrito! | 482,25 kB | Adobe PDF | Ver/Abrir |