http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110473167-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10056 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V2201-03 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-40 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2414 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-13 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-493 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N33-493 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-11 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H30-40 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-774 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-13 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-82 |
| filingDate | 2019-07-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-06-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-06-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-110473167-B |
| titleOfInvention | Urine sediment image recognition system and method based on deep learning |
| abstract | The invention relates to the field of medical image processing, in particular to a system and method for recognizing urine sediment images based on deep learning. The image acquisition module collects the urine sample to obtain the original image; the image segmentation module performs segmentation processing on the original image to obtain a segmented image of the urinary sediment components; the image recognition module based on deep learning recognizes the segmented images of the urinary sediment components, integrates The recognition results of the three network models are used to obtain the output of the image recognition module based on deep learning; the counting module performs statistical processing on the output results to obtain quantitative medical index references; the system outputs the results of the deep learning-based image recognition module and the counting module. the result of. The invention can automatically realize end-to-end feature extraction and classification, effectively extract tiny features that are difficult to be found by naked eyes in the formed components of urine sediment, so as to solve the complex classification problem of 11 kinds of urinary sediment components with high quality, and has strong medical Value. |
| priorityDate | 2019-07-09-04:00^^<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/CID33005 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID452232359 |
Total number of triples: 34.