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pro vyhledávání: '"Zamila Soilihi"'
The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI
Autor:
Ilyass Moummad, Cyril Jaudet, Alexis Lechervy, Samuel Valable, Charlotte Raboutet, Zamila Soilihi, Juliette Thariat, Nadia Falzone, Joëlle Lacroix, Alain Batalla, Aurélien Corroyer-Dulmont
Publikováno v:
Cancers, Vol 14, Iss 1, p 36 (2021)
Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL
Externí odkaz:
https://doaj.org/article/619c4a0fc3774c4d8a74b8a5d271cf05
The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI
Autor:
Ilyass Moummad, Cyril Jaudet, Alexis Lechervy, Samuel Valable, Charlotte Raboutet, Zamila Soilihi, Juliette Thariat, Nadia Falzone, Joëlle Lacroix, Alain Batalla, Aurélien Corroyer-Dulmont
Publikováno v:
Cancers, Vol 14, Iss 36, p 36 (2022)
Cancers
Cancers; Volume 14; Issue 1; Pages: 36
Cancers, 2022, 14 (1), pp.36. ⟨10.3390/cancers14010036⟩
Cancers
Cancers; Volume 14; Issue 1; Pages: 36
Cancers, 2022, 14 (1), pp.36. ⟨10.3390/cancers14010036⟩
Simple Summary Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as r