http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113822903-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_31565373c08678b1a7e68d5a1e4c5878 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-13 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-12 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-12 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-13 |
| filingDate | 2021-07-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ae9d90371428af9ab3ee9438977e8e59 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_73fb38939e996b1ed77de33e21ea0846 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_60ac36c6a45f0ca57beeeaf3d7ff5d55 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b7c62f141e50e5d05e05b70a4eab39f2 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0a18eb6adff71a26f75d3d4d043b4e88 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_78281074b6611de2b98765359fa15f59 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_08eef86f44967b574597fd846d34794e |
| publicationDate | 2021-12-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-113822903-A |
| titleOfInvention | Segmentation model training method, image processing method, device, equipment and medium |
| abstract | The present application discloses a segmentation model training method, image processing method, device, equipment and medium, which belong to the technical field of artificial intelligence. The training method of the segmentation model includes: acquiring a sample image and point labels; acquiring a first channel image; calling a first initial segmentation model to segment the sample image to obtain a first segmentation result; calling a second initial segmentation model to segment the first channel image Perform segmentation to obtain the second segmentation result; obtain the target loss function based on the first segmentation result, the second segmentation result and the point label; use the target loss function to train the first initial segmentation model and the second initial segmentation model to obtain the first A target segmentation model and a second target segmentation model. In this way, the two target segmentation models obtained by training can comprehensively consider the texture features and boundary features of the sub-images, the information considered is more comprehensive, and the training effect of the model is high, which is beneficial to improve the accuracy of the obtained target segmentation results. |
| priorityDate | 2021-07-15-04:00^^<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/CID86598188 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID450235101 |
Total number of triples: 22.