| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_34d6a279e6cdd624c15f390442c3508e |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10104
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10088
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10116
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/G06N3-044
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-5282
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-01 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0454
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-002 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate |
2018-07-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2020-10-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8c4171f544746ca86c98b467c1f2d5a9
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7e18928c8dccf651718f6f76f639a85c
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3f40d7ce56b4f463338e2d43ac2283b6
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_76b7b1165e72e63f7149c691c50f6937
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ad9b82f8d13d1b1d5a66aeba35777911 |
| publicationDate |
2020-10-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-10803555-B2 |
| titleOfInvention |
System and method for determining a trained neural network model for scattering correction |
| abstract |
A method for generating a trained neural network model for scanning correction corresponding to one or more imaging parameters is provided. The trained neural network model may be trained using training data. The training data may include at least one first set of training data. The first set of training data may be generated according to a process for generating the first set of training data. The process may include obtaining a first image and a second image corresponding to the one or more imaging parameters. The second image may include less scattering noises than the first image. The process may further include determine the first set of training data based on the first image and the second image. |
| priorityDate |
2017-08-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |