| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_1fefd66fdbc3870bbaa8e5a4f87bdb0a |
| classificationCPCAdditional |
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/G06T2207-10132
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30101 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-25 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-32
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-10 |
| filingDate |
2019-04-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5c2144bee7cfd8d173d066a376b289ff
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0035d1d6aa256e8dc1c207b7d106ab35
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0adb7260498f6dceaaf2129b6c9c58f5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_04d3ced7660e68e953e0ef03837129dc |
| publicationDate |
2019-08-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-110189299-A |
| titleOfInvention |
A method and system for automatic identification of cerebrovascular events based on MoileNet |
| abstract |
The invention belongs to the intersecting field of computer technology and medical images, and discloses a method and system for automatic identification of cerebrovascular events based on MoileNet, wherein the method includes the steps of: (1) collecting two-dimensional carotid artery initial ultrasonic image data; (2) segmenting Carotid artery adventitia to obtain ROI image data; (3) construct a deep learning network based on MoileNet and conduct training; (4) input ROI image data into the trained deep learning network for testing, and obtain the initial two-dimensional carotid artery The prediction results of the occurrence or non-occurrence of cerebrovascular events corresponding to the ultrasound image data, so as to automatically identify cerebrovascular events. The present invention adopts a deep learning network based on MoileNet, uses deep learning methods to automatically extract ultrasonic carotid artery image features, and automatically recognizes cerebrovascular events, which can effectively solve the problems of strong subjectivity and feature redundancy of manually defined features. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111724365-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111724365-B |
| priorityDate |
2019-04-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |