http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2021155340-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2a7ebd2ce5ef7e691bd44c48a88a2bfc |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10112 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/G06T2207-30068 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-5258 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-002 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-025 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B6-502 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-03 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-02 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B6-00 |
| filingDate | 2021-02-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3173172d7ed577490051f09acec3a167 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_80e6d8acbcf0edd7f16152339b4c0c32 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_38f1222f33b7b03a471e834674b299e8 |
| publicationDate | 2021-08-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | WO-2021155340-A1 |
| titleOfInvention | Systems and methods for artifact reduction in tomosynthesis with multi-scale deep learning image processing |
| abstract | Systems and methods are provided for a multi-scale deep learning-based digital breast tomosynthesis (DBT) image reconstruction that mitigates the superposition of breast tissue along with the limited angular artifacts, and improves in-depth resolution of the resulting images. A multi-scale deep neural network may be used where a first network may focus on a first parameter, such as limited angular artifacts reduction, and a second network may focus on a second parameter, such as image detail refinement. The output from the first neural network may be used as the input for the second neural network. The systems and methods may reduce the sparse-view artifacts in DBT via deep learning without losing image sharpness and contrast. A deep neural network may be trained in a way to reduce training-time computational cost. An ROI loss method may be used for further improvement on the resolution and contrast of the images. |
| priorityDate | 2020-01-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 28.