| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_49b8071052a3ee36a9c0fc8d5a6aaf85
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_4319fe3d827bd24add491d4ed90e7692
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2a7ebd2ce5ef7e691bd44c48a88a2bfc
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_7cc9b52fbbb0be8ecf4fb985fbc01e72 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-507
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30016 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-032
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-501
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-504 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-03
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate |
2019-09-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_108878cb8502ff67cb1124414d007f48
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fb9796080fef39b596798698b6389359
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c7b263f00e2bd67a3bcb096de300070b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ba75f0ea109872fa8b1dced9d1112c33
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_68c4d1bd8ff772f9c1bf662eb758b9cb |
| publicationDate |
2021-04-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2021093258-A1 |
| titleOfInvention |
Computed tomography medical imaging stroke model |
| abstract |
Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate learned medical imaging stroke data regarding a brain anatomical region based on CT data associated with the brain anatomical region and diffusion-weighted imaging (DWI) data associated with one or more segmentation masks for the brain anatomical region. The system also detects presence or absence of a medical stroke condition in a CT image based on the learned medical imaging stroke data. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11357464-B2 |
| priorityDate |
2019-09-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |