| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_00c1356fc82a5edb9b92a452be169826 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10104
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081
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/G06T2207-30096 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00 |
| filingDate |
2018-12-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2020-01-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4bbac98fdeac1873092b47de76da2982
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a3fe559056508b4272fc0a4bf47f6d80
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_77edae5acf043f71c6248efd19a27cf5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_702fa7c894a3e01c25ad03f0a340c906 |
| publicationDate |
2020-01-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
TW-I681406-B |
| titleOfInvention |
Deep learning of tumor image-aided prediction of prognosis of patients with uterine cervical cancer system, method and computer program product thereof |
| abstract |
A deep learning of tumor image-aided prediction of prognosis of patients with uterine cervical cancer system for analyzing an image data of the uterine cervical cancer tumor of a patient is provided. The system includes a data augmentation module and a deep convolution neural network model. The data augmentation module is used to apply a data expansion process to the image data, so as to generate a plurality of slices of the uterine cervical cancer tumor. The deep convolution neural network model is used to apply a feature analysis to the slices, so as to predict the prognosis of a patient after Chemoradiotherapy. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114267443-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114267443-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/TW-I807223-B |
| priorityDate |
2018-12-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |