Predicate |
Object |
assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_3cfc3ddba0550c7f2066a4ee86ab4283 |
classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10024 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30088 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-0077 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B2576-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2021-8887 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/G06N3-045 |
classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-1075 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-445 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-443 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-444 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-1079 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4887 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N21-8851 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0464 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-09 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-40 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30 |
classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H30-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N21-88 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-107 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00 |
filingDate |
2022-06-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_cb9a4ec2d37cf977cbdc343af5ebc9ed http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8b4b2149affdc290cba7b43514e3609a http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_11b93f82dda5eae105636079373d249d http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0f92af9b503a8a6218c37cf4a2edb5de |
publicationDate |
2022-12-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
publicationNumber |
WO-2022251925-A1 |
titleOfInvention |
Method, system and software product for processing skin field images |
abstract |
Methods and computer systems for training neural networks and using the trained neural networks to analyse skin field images are described. Severity of skin damage in a skin field image may be automatically analysed using the disclosed invention. The methods may include annotating an image with a keratoses count score indicative of the quantity of keratoses present in the image; annotating the image with a keratoses thickness score indicative of the thickness of keratoses present in the image, the keratoses thickness score being the highest score assigned to any keratosis present in the image; annotating the image with an area of involvement score indicative of the proportion of the image area affected by keratoses; training a first neural network with the annotated image and the keratosis count score; training a second neural network with the annotated image and the keratosis thickness score; and training a third neural network with the annotated image and the area of involvement score. The trained first, second and third neural networks are then used to assess the severity of skin damage in a skin field image. The disclosed methods and/or computer system may provide the ability to analyse skin field images taken by non-medically-qualified persons and/or non- dedicated medical image capturing devices, such as by using a mobile phone camera. |
priorityDate |
2021-06-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
type |
http://data.epo.org/linked-data/def/patent/Publication |