http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113786185-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d4301b2f242a8e6eac7a90359b4ea92d |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-0042 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-055 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-055 |
| filingDate | 2021-09-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0189c703b328f439d87ae112892f5aa8 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e38159f61d75b61667d7a7958ca92b1f http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5b9f43f94c7ad393de3f847c90502929 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1def955555e5536117b182297295d5e1 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_55c25e525cedc6891818ede6b3a37803 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1613317e4d1868c236bdd2f5064b143a http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8f7a554f5910c680a53ebc52460d36f0 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2ee6dccef1b1836642f84f5034777561 |
| publicationDate | 2021-12-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-113786185-A |
| titleOfInvention | Static brain network feature extraction method and system based on convolutional neural network |
| abstract | The invention discloses a static brain network feature extraction method based on a convolutional neural network, which comprises the following steps: constructing a static brain network by calculating a pearson coefficient between each brain region; processing the static brain network by adopting a convolutional neural network with parameter size of [32,32,64,32] to extract corresponding brain region characteristics; connecting two convolutional layers behind the convolutional neural network, wherein the sizes of convolutional cores of the two convolutional layers are 32 and 16 respectively, and the step length is 116; and (3) sending the brain area features with the dimension of 1x32 after the convolutional layer processing into two full-connection layers with the sizes of 64 and 32 respectively to continuously extract the features, and then adopting a SoftMax logistic regression function to diagnose and classify the brain diseases. The invention can learn more characteristics with discriminative power and interpretability, can obviously improve the brain disease classification performance and has better classification performance for brain disease diagnosis. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116051849-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116051849-B |
| priorityDate | 2021-09-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID977 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419523291 |
Total number of triples: 24.