| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2a819eda0adf22936a52362eeebb9fb4 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10056 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T11-001
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-69
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24137
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-698
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V20-69
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-82 |
| filingDate |
2020-12-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_14f8612bc4b526e844d20ab0d93a5cc7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_bf933dd6e72bb556e02598eabb4da3be
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6c89e09b2cf4239a03a13ba3c44512ee
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d24181845ae548688eeed34d6f792c5b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a38375ae861e9df66c0018e215165dd1
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fb701e1e181e791ebf1dad6f8a783e97
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e6465068e6e5bc39072e0a80c948de73 |
| publicationDate |
2022-08-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-114945954-A |
| titleOfInvention |
Method and system for digital staining of microscopic images using deep learning |
| abstract |
A deep learning-based digital/virtual staining method and system enables the creation of digital/virtually stained microscopic images from unlabeled or unstained samples. In one embodiment, the method uses fluorescence microscopy to generate digital/virtually stained microscopy images of an unlabeled or unstained sample using fluorescence lifetime (FLIM) images of the sample. In another embodiment, a digital/virtual autofocus method using machine learning to generate microscope images with improved focus using a trained deep neural network is provided. In another embodiment, a trained deep neural network generates digital/virtually stained microscopic images of unlabeled or unstained samples with multiple different stains obtained using microscopy. The multiple stains in the output image or sub-regions thereof are substantially equivalent to corresponding microscopic images or image sub-regions of the same specimen that have been histologically stained. |
| priorityDate |
2019-12-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |