Please use this identifier to cite or link to this item:
https://ahro.austin.org.au/austinjspui/handle/1/16790Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Dahan, Ariel | - |
| dc.contributor.author | Gaillard, Frank | en |
| dc.contributor.author | Wang, Wayland | en |
| dc.date | 2017-07-29 | - |
| dc.date.accessioned | 2017-08-10T01:33:54Z | - |
| dc.date.available | 2017-08-10T01:33:54Z | - |
| dc.date.issued | 2018-01 | - |
| dc.identifier.citation | Journal of the American College of Radiology 2018; 15 (1 pt A): 93-96 | en_US |
| dc.identifier.uri | https://ahro.austin.org.au/austinjspui/handle/1/16790 | - |
| dc.title | Computer-aided detection can bridge the skill gap in multiple sclerosis monitoring | en_US |
| dc.type | Journal Article | en_US |
| dc.identifier.journaltitle | Journal of the American College of Radiology | en_US |
| dc.identifier.affiliation | St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia | en_US |
| dc.identifier.affiliation | Department of Radiology, University of Melbourne, The Royal Melbourne Hospital, Parkville, Victoria, Australia | en_US |
| dc.identifier.affiliation | Department of Radiology, Austin Health, Heidelberg, Victoria, Australia | en_US |
| dc.type.studyortrial | Case Series and Case Reports | en_US |
| dc.identifier.pubmeduri | https://pubmed.ncbi.nlm.nih.gov/28764954 | en_US |
| dc.identifier.doi | 10.1016/j.jacr.2017.06.030 | en_US |
| dc.type.content | Text | en_US |
| dc.type.austin | Journal Article | en_US |
| local.name.researcher | Dahan, Ariel | |
| item.grantfulltext | none | - |
| item.openairetype | Journal Article | - |
| item.fulltext | No Fulltext | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
| item.cerifentitytype | Publications | - |
| crisitem.author.dept | Radiology | - |
| Appears in Collections: | Journal articles | |
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