Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90400
Título: | Cloud-based privacy-preserving medical imaging system using machine learning tools |
Autor(es): | Alves, João Soares, Beatriz Brito, Cláudia Vanessa Martins Sousa, António |
Palavras-chave: | Healthcare application DICOM images Cloud computing Machine learning |
Data: | 2022 |
Editora: | Springer |
Revista: | Lecture Notes in Artificial Intelligence (subseries of Lecture Notes in Computer Science) |
Citação: | Alves, J., Soares, B., Brito, C., Sousa, A. (2022). Cloud-Based Privacy-Preserving Medical Imaging System Using Machine Learning Tools. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_17 |
Resumo(s): | Healthcare environments are generating a deluge of sensitive data. Nonetheless, dealing with large amounts of data is an expensive task, and current solutions resort to the cloud environment. Additionally, the intersection of the cloud environment and healthcare data opens new challenges regarding data privacy.With this in mind, we propose MEDCLOUDCARE (MCC), a healthcare application offering medical image viewing and processing tools while integrating cloud computing and AI. Moreover, MCC provides security and privacy features, scalability and high availability. The system is intended for two user groups: health professionals and researchers. The former can remotely view, process and share medical imaging information in the DICOM format. Also, it can use pre-trained Machine Learning (ML) models to aid the analysis of medical images. The latter can remotely add, share, and deploy ML models to perform inference on DICOM images.MCC incorporates a DICOM web viewer enabling users to view and process DICOM studies, which they can also upload and store. Regarding the security and privacy of the data, all sensitive information is encrypted at rest and in transit. Furthermore, MCC is intended for cloud environments. Thus, the system is deployed using Kubernetes, increasing the efficiency, availability and scalability of the ML inference process. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/90400 |
ISBN: | 978-3-031-16473-6 |
e-ISBN: | 978-3-031-16474-3 |
DOI: | 10.1007/978-3-031-16474-3_17 |
ISSN: | 0302-9743 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-031-16474-3_17 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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EPIA_2022_PAPER (1).pdf | 1,01 MB | Adobe PDF | Ver/Abrir |