| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_966a6c1f382ec9bcb5e3839dfd73e5b8 |
| 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/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30024
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2200-24 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-698
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-695
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2414
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-7715
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-454 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 |
| filingDate |
2020-11-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_313d4d837d67c858325f4c8fcc17d1e6
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ec736d23aa6e312d9c21a36c9d31a822
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_befbc4473a06ebcdbf836ded8baa3c36 |
| publicationDate |
2022-07-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-114730463-A |
| titleOfInvention |
A multi-instance learner for organizational image classification |
| abstract |
The present invention relates to a method for classifying tissue images. The method comprises: - receiving (102) a plurality of digitally organized images; - splitting (104) each received image into a set of image blocks; - for each of the blocks, from the blocks extracting (106) feature vectors from;-providing (108) a multiple instance learning (MIL) program configured to use a model to interpret the input image based on feature vectors extracted from all patches of any image Classified as a member of one of at least two different classes; - for each of the blocks, computing (110) a certainty value indicating the pair of feature vectors of the model with respect to the block Determinism of the contribution of the classification of the image; - for each image in the image, using (114) a pooling function based on a deterministic value by the MIL procedure as the determination of the block of the image to aggregate the feature vector of the image or the predicted value calculated from the feature vector of the image into an aggregated predicted value; and - base each image in the image based on the aggregated predicted value Predictors are classified (116) as members of one of the classes. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116030303-A |
| priorityDate |
2019-11-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |