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Article dans une revue

Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation

Abstract : With the growth of medical data stored as bases for researches and diagnosis tasks, healthcare providers are in need of automatic processing methods to make accurate and fast image analysis such as segmentation or restoration. Most of the existing solutions to deal with these tasks are based on Deep Learning methods that require the use of powerful dedicated hardware to be executed and address a power consumption problem that is not compatible with the aforementioned requests. There is thus a demand in the development of low-cost image analysis systems with increased performances. In this work, we address this problem by proposing a fully-automatic brain tumor segmentation method based on a Convolutional Neural Network, executed by a low-cost, Deep Learning ready GPU embedded platform. We validated our approach using the BRaTS 2015 dataset to segment brain tumors and proved that an artificial neural network can be trained and used in the medical field with limited resources by redefining some of its inner operations.
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Soumis le : mercredi 6 avril 2022 - 10:41:24
Dernière modification le : mercredi 31 août 2022 - 16:42:20




Brad Niepceron, Ahmed Nait-Sidi-Moh, Filippo Grassia. Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation. Applied Artificial Intelligence, Taylor & Francis, 2020, 34 (12), pp.866-879. ⟨10.1080/08839514.2020.1787678⟩. ⟨hal-03632175⟩



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