Guiding AI-based classification: can conventional functional neuroimaging analysis improve deep learning methods for identifying risk for essential hypertension?
dc.contributor.author | Nordanger, Emma | |
dc.contributor.author | Chau, Christina | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för elektroteknik | sv |
dc.contributor.examiner | Monti, Paulo | |
dc.date.accessioned | 2021-10-11T10:46:36Z | |
dc.date.available | 2021-10-11T10:46:36Z | |
dc.date.issued | 2021 | sv |
dc.date.submitted | 2020 | |
dc.identifier.coursecode | EENX30 | sv |
dc.identifier.uri | https://hdl.handle.net/20.500.12380/304228 | |
dc.language.iso | eng | sv |
dc.setspec.uppsok | Technology | |
dc.title | Guiding AI-based classification: can conventional functional neuroimaging analysis improve deep learning methods for identifying risk for essential hypertension? | sv |
dc.type.degree | Examensarbete för masterexamen | sv |
dc.type.uppsok | H | |
local.programme | Biomedical engineering (MPBME), MSc |