Ivanova et al., 2023 - Google Patents
Empowering renal cancer management with AI and digital pathology: Pathology, diagnostics and prognosisIvanova et al., 2023
View HTML- Document ID
- 16150084179975733481
- Author
- Ivanova E
- Fayzullin A
- Grinin V
- Ermilov D
- Arutyunyan A
- Timashev P
- Shekhter A
- Publication year
- Publication venue
- Biomedicines
External Links
Snippet
Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay
- G01N33/574—Immunoassay; Biospecific binding assay for cancer
- G01N33/57407—Specifically defined cancers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shmatko et al. | Artificial intelligence in histopathology: enhancing cancer research and clinical oncology | |
Calderaro et al. | Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers | |
Davri et al. | Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review | |
Rathore et al. | Glioma grading via analysis of digital pathology images using machine learning | |
Alvarez-Jimenez et al. | Identifying cross-scale associations between radiomic and pathomic signatures of non-small cell lung cancer subtypes: preliminary results | |
Steinbuss et al. | Deep learning for the classification of non-Hodgkin lymphoma on histopathological images | |
Godin et al. | A novel approach for quantifying cancer cells showing hybrid epithelial/mesenchymal states in large series of tissue samples: towards a new prognostic marker | |
Ahmed et al. | Deep learning approaches in histopathology | |
Murchan et al. | Deep learning of histopathological features for the prediction of tumour molecular genetics | |
Rathore et al. | Segmentation and grade prediction of colon cancer digital pathology images across multiple institutions | |
Shim et al. | DeepRePath: identifying the prognostic features of early-stage lung adenocarcinoma using multi-scale pathology images and deep convolutional neural networks | |
Ivanova et al. | Empowering renal cancer management with AI and digital pathology: Pathology, diagnostics and prognosis | |
Chelebian et al. | Morphological features extracted by AI associated with spatial transcriptomics in prostate cancer | |
Ghosh et al. | The potential of artificial intelligence to detect lymphovascular invasion in testicular cancer | |
Mandair et al. | Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology | |
Jarkman et al. | Generalization of deep learning in digital pathology: experience in breast cancer metastasis detection | |
Couture | Deep learning-based prediction of molecular tumor biomarkers from H&E: a practical review | |
Kim et al. | Artificial intelligence techniques for prostate cancer detection through dual-channel tissue feature engineering | |
Schiele et al. | Deep learning prediction of metastasis in locally advanced colon cancer using binary histologic tumor images | |
Lagree et al. | Assessment of digital pathology imaging biomarkers associated with breast cancer histologic grade | |
Salvi et al. | Impact of stain normalization on pathologist assessment of prostate cancer: a comparative study | |
Vranes et al. | Size and shape filtering of malignant cell clusters within breast tumors identifies scattered individual epithelial cells as the most valuable histomorphological clue in the prognosis of distant metastasis risk | |
Mosquera-Zamudio et al. | Deep learning for skin melanocytic tumors in whole-slide images: A systematic review | |
Khoraminia et al. | Artificial intelligence in digital pathology for bladder cancer: Hype or hope? a systematic review | |
Lee et al. | Ensemble deep learning model to predict lymphovascular invasion in gastric cancer |