| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_ca46be4e68fa1f5a8b2ef4ab654c490a |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30068
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20101
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-187
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/G06T7-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H30-20
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/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-187
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/G16H30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate |
2018-12-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8bbae01d294ea2fcb574c517887c1950
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b6314d5758b236fe90f2bc54e66475a7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_63729f20360a8dca306a9d56ca97b54e
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3ecfaa641041efaf6183bfb17c92d638
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ad71e7f6ce07720c3d30072c5f79beb6
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2e8d1e336a858adc06eb17ce0a56137b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4e789fdb81d8273c510a7abd6e1f4230 |
| publicationDate |
2019-04-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-109671060-A |
| titleOfInvention |
Computer-aided breast mass detection method based on selective search and CNN |
| abstract |
The invention proposes a computer-aided breast mass detection method based on selective search and CNN, which is used to solve the technical problem of low detection accuracy caused by poor candidate frame quality and low classification accuracy in the prior art. The steps are: 1. Acquiring multiple mammography X-ray images and their physician's annotation files; 2. Preprocessing N mammography X-ray images; 3. Obtaining N preprocessed mammography X-ray images 4. Obtain the candidate frame set of N preprocessed mammography images based on the selective search algorithm; 5. Construct a convolutional neural network CNN and initialize it; 6. Analyze the initialized convolutional neural network The network is trained; 7. Obtain the mass area of the mammography image to be detected. The breast mass detection method of the invention has high detection rate and low false positive rate, and can be applied to a computer-aided breast mass detection system. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111667491-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111667491-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113421240-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112766181-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112766181-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110110722-A |
| priorityDate |
2018-12-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |