Wang et al., 2019 - Google Patents
Graph convolutional nets for tool presence detection in surgical videosWang et al., 2019
View PDF- Document ID
- 10516134212560914446
- Author
- Wang S
- Xu Z
- Yan C
- Huang J
- Publication year
- Publication venue
- International Conference on Information Processing in Medical Imaging
External Links
Snippet
Surgical tool presence detection is one of the key problems in automatic surgical video content analysis. Solving this problem benefits many applications such as the evaluation of surgical instrument usage and automatic surgical report generation. Given the fact that each …
- 238000001514 detection method 0 title abstract description 46
Classifications
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
- G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
-
- 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
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | Graph convolutional nets for tool presence detection in surgical videos | |
Wang et al. | Inferring salient objects from human fixations | |
Ni et al. | Raunet: Residual attention u-net for semantic segmentation of cataract surgical instruments | |
Yan et al. | 3D context enhanced region-based convolutional neural network for end-to-end lesion detection | |
BenTaieb et al. | Predicting cancer with a recurrent visual attention model for histopathology images | |
Codella et al. | Collaborative human-AI (CHAI): Evidence-based interpretable melanoma classification in dermoscopic images | |
Chen et al. | Bridging computational features toward multiple semantic features with multi-task regression: A study of CT pulmonary nodules | |
Dutta et al. | Evaluation of the impact of deep learning architectural components selection and dataset size on a medical imaging task | |
Prasad et al. | Lung cancer detection and classification using deep neural network based on hybrid metaheuristic algorithm | |
Manikandan et al. | Cataract Fundus Image Detection Using Hybrid Deep Learning Model | |
Tao et al. | LAST: LAtent space-constrained transformers for automatic surgical phase recognition and tool presence detection | |
Van Molle et al. | Quantifying uncertainty of deep neural networks in skin lesion classification | |
Banerjee et al. | Deep belief convolutional neural network with artificial image creation by gans based diagnosis of pneumonia in radiological samples of the pectoralis major | |
Jaafari et al. | The impact of ensemble learning on surgical tools classification during laparoscopic cholecystectomy | |
Nayak et al. | Non-linear cellular automata based edge detector for optical character images | |
Wang et al. | Ensemble of multi-sized FCNs to improve white matter lesion segmentation | |
Dash et al. | An interactive machine learning approach for brain tumor MRI segmentation | |
Budhwant et al. | Open-set recognition for skin lesions using dermoscopic images | |
Lin et al. | FocAnnot: patch-wise active learning for intensive cell image segmentation | |
Shi et al. | Mitigating biases in long-tailed recognition via semantic-guided feature transfer | |
Priyanka Pramila et al. | Automated skin lesion detection and classification using fused deep convolutional neural network on dermoscopic images | |
Chheda et al. | Gastrointestinal tract anomaly detection from endoscopic videos using object detection approach | |
Yu et al. | Anomaly detection in high-dimensional data based on autoregressive flow | |
Stanescu et al. | A comparative study of some methods for color medical images segmentation | |
Sathyan et al. | Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images |