Ruini et al., 2021 - Google Patents
Machine learning based prediction of squamous cell carcinoma in ex vivo confocal laser scanning microscopyRuini et al., 2021
View HTML- Document ID
- 4802126236256413704
- Author
- Ruini C
- Schlingmann S
- Jonke Ĺ
- Avci P
- PadrĂłn-Laso V
- Neumeier F
- Koveshazi I
- Ikeliani I
- Patzer K
- Kunrad E
- Kendziora B
- Sattler E
- French L
- Hartmann D
- Publication year
- Publication venue
- Cancers
External Links
Snippet
Simple Summary Squamous cell carcinoma is the second most common type of skin cancer, with incidence rates rising each year. Micrographic urgery is the treatment of choice for large, aggressive, or recurrent lesions. To ensure complete removal, excised tissue is frozen …
- 238000001218 confocal laser scanning microscopy 0 title abstract description 71
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
-
- 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
-
- 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
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- 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
- G06F17/20—Handling natural language data
-
- 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
-
- 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
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Camalan et al. | Convolutional neural network-based clinical predictors of oral dysplasia: Class activation map analysis of deep learning results | |
Sahoo et al. | An improvised deep-learning-based mask R-CNN model for laryngeal cancer detection using CT images | |
Gallego et al. | Glomerulus classification and detection based on convolutional neural networks | |
Musulin et al. | An enhanced histopathology analysis: An ai-based system for multiclass grading of oral squamous cell carcinoma and segmenting of epithelial and stromal tissue | |
Ailia et al. | Current trend of artificial intelligence patents in digital pathology: a systematic evaluation of the patent landscape | |
Ruini et al. | Machine learning based prediction of squamous cell carcinoma in ex vivo confocal laser scanning microscopy | |
Shovon et al. | Strategies for enhancing the multi-stage classification performances of her2 breast cancer from hematoxylin and eosin images | |
Veronese et al. | The role in teledermoscopy of an inexpensive and easy-to-use smartphone device for the classification of three types of skin lesions using convolutional neural networks | |
Liu et al. | Breast histopathological image classification method based on autoencoder and siamese framework | |
La Salvia et al. | Deep convolutional generative adversarial networks to enhance artificial intelligence in healthcare: a skin cancer application | |
Patel et al. | Analysis of artificial intelligence-based approaches applied to non-invasive imaging for early detection of melanoma: a systematic review | |
Yu et al. | Machine learning based on morphological features enables classification of primary intestinal T-cell lymphomas | |
Zehra et al. | A novel deep learning-based mitosis recognition approach and dataset for uterine leiomyosarcoma histopathology | |
Xu et al. | Generative adversarial networks can create high quality artificial prostate cancer magnetic resonance images | |
Jansen et al. | Evaluation of a deep learning approach to differentiate Bowen’s disease and seborrheic keratosis | |
Qasmieh et al. | Automated detection of corneal ulcer using combination image processing and deep learning | |
Naeem et al. | SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images | |
Sauter et al. | Validating automatic concept-based explanations for AI-based digital histopathology | |
Minami et al. | Diagnosis of depth of submucosal invasion in colorectal cancer with AI using deep learning | |
Teramoto et al. | Detection and characterization of gastric cancer using cascade deep learning model in endoscopic images | |
Su et al. | Multi-scale attention convolutional network for Masson stained bile duct segmentation from liver pathology images | |
Hao et al. | Accurate kidney pathological image classification method based on deep learning and multi-modal fusion method with application to membranous nephropathy | |
Semerci et al. | The Role of Artificial Intelligence in Early Diagnosis and Molecular Classification of Head and Neck Skin Cancers: A Multidisciplinary Approach | |
Lee et al. | Deep-learning-enabled computer-aided diagnosis in the classification of pancreatic cystic lesions on confocal laser endomicroscopy | |
Yu et al. | A deep-learning-based artificial intelligence system for the pathology diagnosis of uterine smooth muscle tumor |