http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112150478-B
Outgoing Links
| Predicate | Object |
|---|---|
| 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/G06T2207-10088 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-143 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-136 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-136 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-143 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-11 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate | 2020-08-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2021-06-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2021-06-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-112150478-B |
| titleOfInvention | A method and system for constructing a semi-supervised image segmentation framework |
| abstract | The invention provides a method for constructing a semi-supervised image segmentation framework, including constructing a semi-supervised image segmentation framework including a student model, a teacher model and a discriminator; Segmentation loss; obtain the original unlabeled MRI image and the noise unlabeled MRI image after combining it with the noise of the preset Gaussian distribution, to obtain the corresponding student segmentation probability result map and teacher segmentation probability result map, and then overlaid on the original. On the unlabeled MRI image, the student segmentation area and the teacher segmentation area are generated and passed to the discriminator for similarity comparison to calculate the consistency loss; according to the supervised segmentation loss and consistency loss, the total segmentation loss is obtained and half Supervised image segmentation framework for optimization. The present invention is implemented by improving the mean teacher model to establish a general semi-supervised segmentation framework that can be used for 3D medical images, without additional image-level labeling. |
| priorityDate | 2020-08-31-04:00^^<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/SID482532689 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID23985 |
Total number of triples: 23.