http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110414631-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V2201-03 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-241 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-82 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-774 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate | 2019-09-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-02-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-02-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-110414631-B |
| titleOfInvention | Medical image-based lesion detection method, model training method and device |
| abstract | The present application discloses a medical image-based lesion detection method. The method is applied in the field of artificial intelligence, and can be specifically applied in the field of intelligent medicine. The method includes: acquiring an image of a mammography target to be predicted; The probability value of each pixel in the target image belonging to the lesion, the main task network model is obtained by training the source domain data set and the domain classification network model, and the domain classification network model is obtained by training the source domain data set and the target domain data set ; Generate the mass detection result of the mammography image to be predicted according to the probability value that each pixel belongs to the lesion. The present application also provides a method and apparatus for model training. This application utilizes the relationship between the main task network model and the domain classification network model to solve the domain difference problem between the source domain data set and the target domain data set, so that the main task network model can obtain excellent detection performance on the target data set. |
| priorityDate | 2019-01-29-04:00^^<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/CID23932 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419405613 |
Total number of triples: 19.