| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_60442fdc20fd71481fc3d6b8cdee2c31
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_c72d118f5664072de841f9c5c34b9d99
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_23a4e0c875bf1ac2b8a3b76517de6c7d |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10072
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2211-424
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2211-421
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2223-419 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N23-046
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T11-003
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T11-008
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T11-00 |
| filingDate |
2018-10-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e3b48f9fb1fe69638a68c411df66a775
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a963573b1146a32ff6bc58e690ce69e7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4c1fc4cf8e6cf516c3c0344f4cd712e0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0a58311a6d04cb197f8f2c8abf43fe3e
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e411ea7f000b311798c221d4c5b07770
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6fc68f0faa8a82c517b758c9c14e3f30 |
| publicationDate |
2020-08-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
EP-3695382-A1 |
| titleOfInvention |
Image generation using machine learning |
| abstract |
The present invention relates to driving a machine learning algorithm for image generation and using such a trained algorithm for image generation. The training of the machine learning algorithm may involve using multiple images produced from a single set of tomographic or image projection data (such as simple reconstruction and computationally intensive reconstruction), an image being the target image that has the desired characteristics for the final result. The trained machine learning algorithm can be used to generate a final image corresponding to a computationally intensive algorithm from an input image generated using a less computationally intensive algorithm. |
| priorityDate |
2017-10-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |