| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_53c911e75ea09dd4c2705db5306dae77 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10088
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-34084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30096 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-055
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-46
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-241
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01R33-56341
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-174 |
| classificationIPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01R33-34 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01R33-46
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01R33-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01R33-563
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-055 |
| filingDate |
2020-01-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9da19c6fe61fe3f4e98275b918775dc0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4b9ad2fb08b8db6182330ee73a2e3d82 |
| publicationDate |
2020-09-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2020275857-A1 |
| titleOfInvention |
Tumor Tissue Characterization using Multi-Parametric Magnetic Resonance Imaging |
| abstract |
Brain tumor or other tissue classification and/or segmentation is provided based on from multi-parametric MRI. MRI spectroscopy, such as in combination with structural and/or diffusion MRI measurements, are used to classify. A machine-learned model or classifier distinguishes between the types of tissue in response to input of the multi-parametric MRI. To deal with limited training data for tumors, a patch-based system may be used. To better assist physicians in interpreting results, a confidence map may be generated using the machine-learned classifier. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112365496-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11275976-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2021097682-A1 |
| priorityDate |
2019-03-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |