Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Stéphane Sockeel"'
Autor:
Rémy Peyret, Nicolas Pozin, Stéphane Sockeel, Solène-Florence Kammerer-Jacquet, Julien Adam, Claire Bocciarelli, Yoan Ditchi, Christophe Bontoux, Thomas Depoilly, Loris Guichard, Elisabeth Lanteri, Marie Sockeel, Sophie Prévot
Publikováno v:
PLOS Digital Health, Vol 2, Iss 2, p e0000091 (2023)
Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intellig
Externí odkaz:
https://doaj.org/article/0c285490c3ab42778f496d3b93625fcc
Publikováno v:
PLoS ONE, Vol 11, Iss 1, p e0146845 (2016)
Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based p
Externí odkaz:
https://doaj.org/article/2c0c8c531ffd48e2bf1fc030d48d4e62
Autor:
Nicolas Pozin, Elisabeth Lanteri, Julien Adam, Catherine Miquel, Marie Sockeel, Stéphane Sockeel, Marceau Clavel
Early detection of breast cancer through mammography is crucial for successful treatment. Microcalcifications are small deposits of calcium in breast ducts, they can be an indication of breast cancer and are first detected by mammography. However, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3e5cf62159cca7662b4ee5188b78d29
https://doi.org/10.36227/techrxiv.21981614.v1
https://doi.org/10.36227/techrxiv.21981614.v1
Publikováno v:
PLoS ONE
PLoS ONE, Public Library of Science, 2016, 11 (1), pp.e0146845. ⟨10.1371/journal.pone.0146845⟩
PLoS ONE, Vol 11, Iss 1, p e0146845 (2016)
PLoS ONE, 2016, 11 (1), pp.e0146845. ⟨10.1371/journal.pone.0146845⟩
PLoS ONE, Public Library of Science, 2016, 11 (1), pp.e0146845. ⟨10.1371/journal.pone.0146845⟩
PLoS ONE, Vol 11, Iss 1, p e0146845 (2016)
PLoS ONE, 2016, 11 (1), pp.e0146845. ⟨10.1371/journal.pone.0146845⟩
International audience; Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::701bf7fc6c738149073259742d4e3256
https://hal.sorbonne-universite.fr/hal-01274101/file/journal.pone.0146845.pdf
https://hal.sorbonne-universite.fr/hal-01274101/file/journal.pone.0146845.pdf