Wang et al., 2022 - Google Patents
An uncertainty-aware transformer for MRI cardiac semantic segmentation via mean teachersWang et al., 2022
View PDF- Document ID
- 15556271011941582178
- Author
- Wang Z
- Zheng J
- Voiculescu I
- Publication year
- Publication venue
- Annual Conference on Medical Image Understanding and Analysis
External Links
Snippet
Deep learning methods have shown promising performance in medical image semantic segmentation. The cost of high-quality annotations, however, is still high and hard to access as clinicians are pressed for time. In this paper, we propose to utilize the power of Vision …
- 230000011218 segmentation 0 title abstract description 10
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
- 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
- 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
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Bai et al. | Semi-supervised learning for network-based cardiac MR image segmentation | |
Li et al. | Shape-aware semi-supervised 3D semantic segmentation for medical images | |
Wang et al. | Tripled-uncertainty guided mean teacher model for semi-supervised medical image segmentation | |
Karimi et al. | Convolution-free medical image segmentation using transformers | |
Wang et al. | An uncertainty-aware transformer for MRI cardiac semantic segmentation via mean teachers | |
Liu et al. | Self-supervised mean teacher for semi-supervised chest x-ray classification | |
Huang et al. | Temporal HeartNet: towards human-level automatic analysis of fetal cardiac screening video | |
Wang et al. | When cnn meet with vit: Towards semi-supervised learning for multi-class medical image semantic segmentation | |
Liu et al. | Unsupervised ensemble strategy for retinal vessel segmentation | |
Selvan et al. | Uncertainty quantification in medical image segmentation with normalizing flows | |
Meng et al. | Regression of instance boundary by aggregated CNN and GCN | |
Saeed et al. | TMSS: an end-to-end transformer-based multimodal network for segmentation and survival prediction | |
Liu et al. | Act: Semi-supervised domain-adaptive medical image segmentation with asymmetric co-training | |
Zeng et al. | Reciprocal learning for semi-supervised segmentation | |
Wang et al. | Medical matting: a new perspective on medical segmentation with uncertainty | |
Lourenço-Silva et al. | Using soft labels to model uncertainty in medical image segmentation | |
Meng et al. | Shape-aware weakly/semi-supervised optic disc and cup segmentation with regional/marginal consistency | |
Barash et al. | Automated quantitative assessment of oncological disease progression using deep learning | |
Chang et al. | Soft-label guided semi-supervised learning for bi-ventricle segmentation in cardiac cine MRI | |
Zhong et al. | Semi-supervised pathological image segmentation via cross distillation of multiple attentions | |
Adiga Vasudeva et al. | Leveraging labeling representations in uncertainty-based semi-supervised segmentation | |
Liu et al. | AHU-MultiNet: Adaptive loss balancing based on homoscedastic uncertainty in multi-task medical image segmentation network | |
Yang et al. | A dense R‐CNN multi‐target instance segmentation model and its application in medical image processing | |
Wang et al. | Exigent examiner and mean teacher: An advanced 3d cnn-based semi-supervised brain tumor segmentation framework | |
Silva-Rodríguez et al. | Towards foundation models and few-shot parameter-efficient fine-tuning for volumetric organ segmentation |