Chen, 2023 - Google Patents
Novel Computational Pathology-Based Prognostic Biomarkers for Glomerular Diseases Through Deep PhoenotypingChen, 2023
View PDF- Document ID
- 5925062906738768144
- Author
- Chen Y
- Publication year
External Links
Snippet
In the pursuit of enhancing prognostic methodologies for glomerular diseases, this dissertation introduces a comprehensive approach termed" deep phenotyping," which synergistically integrates artificial intelligence (AI) capabilities with engineered techniques to …
- 201000010099 disease 0 title abstract description 41
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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
-
- 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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Duggento et al. | Deep computational pathology in breast cancer | |
US11645753B2 (en) | Deep learning-based multi-site, multi-primitive segmentation for nephropathology using renal biopsy whole slide images | |
Zeng et al. | Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning | |
Salvi et al. | A hybrid deep learning approach for gland segmentation in prostate histopathological images | |
Barisoni et al. | Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology | |
US8391575B2 (en) | Automatic image analysis and quantification for fluorescence in situ hybridization | |
CN113366530A (en) | Tumor review and post-operative tumor margin assessment in computer-supported histological images | |
Langner et al. | Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants | |
CN115210817A (en) | System and method for analyzing electronic images for quality control | |
Janik et al. | Interpretability of a deep learning model in the application of cardiac MRI segmentation with an ACDC challenge dataset | |
US8542899B2 (en) | Automatic image analysis and quantification for fluorescence in situ hybridization | |
US20230068571A1 (en) | System and method of managing workflow of examination of pathology slides | |
Joseph et al. | Quantitative and qualitative evaluation of deep learning automatic segmentations of corneal endothelial cell images of reduced image quality obtained following cornea transplant | |
CN111986148A (en) | Quick Gleason scoring system for digital pathological image of prostate | |
Kromp et al. | Deep Learning architectures for generalized immunofluorescence based nuclear image segmentation | |
CN110838094A (en) | Pathological section staining style conversion method and electronic equipment | |
Chen | Novel Computational Pathology-Based Prognostic Biomarkers for Glomerular Diseases Through Deep Phoenotyping | |
KR20220060127A (en) | Method for predicting prognosis based on deep-learning and apparatus thereof | |
Hasan et al. | Real-time segmentation and classification of whole-slide images for tumor biomarker scoring | |
Salvi et al. | Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase | |
Liu et al. | Classes U-Net: A method for nuclei segmentation of photoacoustic histology imaging based on information entropy image classification | |
CN115019045B (en) | Small data thyroid ultrasound image segmentation method based on multi-component neighborhood | |
US20230274562A1 (en) | Bootstrapped semantic preprocessing for medical datasets | |
KR102682730B1 (en) | Method for generating representative lesion images of pathological diagnosis case, and computing system performing the same | |
US12087454B2 (en) | Systems and methods for the detection and classification of biological structures |