http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110674866-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30068 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/G06T2207-10081 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-5211 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-52 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-253 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-502 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-5205 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2019-09-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2021-05-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2021-05-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-110674866-B |
| titleOfInvention | Transfer Learning Feature Pyramid Network for X-ray Breast Lesion Image Detection |
| abstract | The present invention proposes a method for detecting X-ray breast lesion images with a transfer learning feature pyramid network, including: step 1, establishing a source domain and target domain data set; step 2, using deformable convolution and extended residual network modules The module establishes a residual network layer of deformable convolution; step 3, combines the residual network layer of deformable convolution to establish a multi-scale feature extraction sub-network based on feature pyramid structure through feature map upsampling and feature fusion methods; step 4 , establish a deformable pooling sub-network that is sensitive to the location of the lesion; step 5, establish a post-processing network layer to optimize the prediction results and loss function; step 6, transfer the training model to the small sample mammography target X-ray breast lesion detection task, to Improve the detection accuracy of the network model for lesions in small sample images. The invention combines the migration learning strategy to realize the image processing of the lesions in the small sample medical image. |
| priorityDate | 2019-09-23-04:00^^<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/substance/SID419405613 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID23932 |
Total number of triples: 27.