http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110223285-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30104 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10088 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/G06T2207-30016 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-337 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0016 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-33 |
| filingDate | 2019-06-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2021-02-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2021-02-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-110223285-B |
| titleOfInvention | Image result prediction method and system based on animal migration and neural network model |
| abstract | The invention discloses an image result prediction method and system based on animal migration and neural network models, which comprises the following steps: the method for acquiring animal image data and patient image data of the same disease by using an animal model of the same disease comprises the following steps: CTP image before treatment, DWI-MRI image after treatment or infarction; acquiring training data of a segmentation network, training the segmentation network, and generating four perfusion segmentation maps of CBF, CBV, TTP and TTD of each sample by using the trained segmentation network; the first classification network is trained using the four perfusion segmentations of the sample, as well as the treated DWI-MRI images or post-infarct DWI-MRI images, and the second classification network is trained using the four perfusion segmentations, mRS scores and the 90-day mortality data for each patient sample in each group of patients. The method realizes result prediction of different treatment methods, and provides theoretical support for treatment of patients. |
| priorityDate | 2019-06-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 24.