Chang et al., 2021 - Google Patents
Data curation challenges for artificial intelligenceChang et al., 2021
- Document ID
- 3310949149726730410
- Author
- Chang K
- Gidwani M
- Patel J
- Li M
- Kalpathy-Cramer J
- Publication year
- Publication venue
- Auto-Segmentation for Radiation Oncology
External Links
Snippet
Deep learning algorithms have brought on a paradigm shift to automated medical image analysis approaches including segmentation. While state-of-the-art models can achieve near human-like performance on many tasks, these same algorithms can be remarkably …
- 230000011218 segmentation 0 abstract description 18
Classifications
-
- 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
- G06F19/321—Management of medical image data, e.g. communication or archiving systems such as picture archiving and communication systems [PACS] or related medical protocols such as digital imaging and communications in medicine protocol [DICOM]; Editing of medical image data, e.g. adding diagnosis information
-
- 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
- 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
- G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
-
- 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
- G06T2207/30048—Heart; Cardiac
-
- 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
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- 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
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- 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
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- 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
- G06Q50/22—Health care, e.g. hospitals; Social work
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mazurowski et al. | Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI | |
| US9275451B2 (en) | Method, a system, and an apparatus for using and processing multidimensional data | |
| Liu | Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature | |
| Bahloul et al. | Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning | |
| Jiang et al. | Deep learning–based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images | |
| Rubin et al. | Biomedical imaging informatics | |
| Lan et al. | Potential roles of transformers in brain tumor diagnosis and treatment | |
| Kumar | Deep learning for multi-modal medical imaging fusion: Enhancing diagnostic accuracy in complex disease detection | |
| Gil et al. | Deep learning-based feature extraction from whole-body PET/CT employing maximum intensity projection images: preliminary results of lung cancer data | |
| Nobashi et al. | Performance comparison of individual and ensemble CNN models for the classification of brain 18F-FDG-PET scans | |
| Abbasi et al. | Unsupervised deep learning registration model for multimodal brain images | |
| US12033755B2 (en) | Method and arrangement for identifying similar pre-stored medical datasets | |
| Boadla et al. | Multimodal cardiac imaging revisited by artificial intelligence: an innovative way of assessment or just an aid? | |
| Theek et al. | Automation of data analysis in molecular cancer imaging and its potential impact on future clinical practice | |
| Badr | Images in space and time: real big data in healthcare | |
| Chang et al. | Data curation challenges for artificial intelligence | |
| Abir et al. | Deep neural networks in medical imaging: advances, challenges, and future directions for precision healthcare | |
| Zhou et al. | Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer | |
| Carmo et al. | Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide | |
| Chang et al. | 14 Data Curation Challenges for Artifcial Intelligence | |
| WO2024220830A1 (en) | Machine learning enabled longitudinal analysis of positron emission tomography and computed tomography scans for assessment of disease progression and treatment response | |
| Shaikh et al. | Radiomics as applied in precision medicine | |
| Hirsch | Artificial Intelligence in Diagnostic Imaging and Radiation Therapy. | |
| EP3965117A1 (en) | Multi-modal computer-aided diagnosis systems and methods for prostate cancer | |
| HU231678B1 (en) | Method for training a system for assisting the evaluation of medical image |