| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_35f3a3319e70f663989afc6c2c772dd7 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30242
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-52
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-7028
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30024 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-35
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2414
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-337
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N1-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-136
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-698
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-695 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764 |
| filingDate |
2019-12-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6be1b218260edb7af649398a4d606d89
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1ca249a8fd1358e8c01580e465f40dd2
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4a685f1d77047141518c1f93896eb44c
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_229467299c2052b86710a3e9f2c90641 |
| publicationDate |
2020-06-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
EP-3663979-A1 |
| titleOfInvention |
A deep learning method for predicting patient response to a therapy |
| abstract |
Disclosed is anin vitromethod for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold. Further disclosed is anin vitromethod for indicating a survival probability of the cancer patient with the same features. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022226284-A1 |
| priorityDate |
2018-12-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |