Mahmood et al., 2018 - Google Patents
Unsupervised reverse domain adaptation for synthetic medical images via adversarial trainingMahmood et al., 2018
View PDF- Document ID
- 512844722600629993
- Author
- Mahmood F
- Chen R
- Durr N
- Publication year
- Publication venue
- IEEE transactions on medical imaging
External Links
Snippet
To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire due to privacy issues, lack of experts available for annotation, underrepresentation of rare conditions, and poor …
- 230000004301 light adaptation 0 title abstract description 26
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
- 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
- G06T2207/10024—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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
- G06T2207/20076—Probabilistic 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
- G06T2207/10072—Tomographic images
-
- 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
-
- 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
- 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
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0068—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Mahmood et al. | Unsupervised reverse domain adaptation for synthetic medical images via adversarial training | |
Nalepa et al. | Data augmentation for brain-tumor segmentation: a review | |
Maier et al. | A gentle introduction to deep learning in medical image processing | |
Münzer et al. | Content-based processing and analysis of endoscopic images and videos: A survey | |
Freedman et al. | Detecting deficient coverage in colonoscopies | |
Mahmood et al. | Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images | |
Zhou et al. | Real-time dense reconstruction of tissue surface from stereo optical video | |
Zhang et al. | A survey on deep learning of small sample in biomedical image analysis | |
Wang et al. | Annotation-efficient learning for medical image segmentation based on noisy pseudo labels and adversarial learning | |
Gu et al. | VINet: A visually interpretable image diagnosis network | |
Park et al. | Recent development of computer vision technology to improve capsule endoscopy | |
Oliveira et al. | Deep transfer learning for segmentation of anatomical structures in chest radiographs | |
Luo et al. | Unsupervised learning of depth estimation from imperfect rectified stereo laparoscopic images | |
Wei et al. | Stereo dense scene reconstruction and accurate localization for learning-based navigation of laparoscope in minimally invasive surgery | |
CN116997928A (en) | Method and apparatus for generating anatomical model using diagnostic image | |
Avila-Montes et al. | Segmentation of the thoracic aorta in noncontrast cardiac CT images | |
Xi et al. | Recovering dense 3D point clouds from single endoscopic image | |
Yang et al. | Reconstruct dynamic soft-tissue with stereo endoscope based on a single-layer network | |
Yang et al. | A geometry-aware deep network for depth estimation in monocular endoscopy | |
Chen et al. | FRSR: Framework for real-time scene reconstruction in robot-assisted minimally invasive surgery | |
Song et al. | Combining deep learning with geometric features for image-based localization in the Gastrointestinal tract | |
Yang et al. | 3D reconstruction from endoscopy images: A survey | |
Liu et al. | Sparse-to-dense coarse-to-fine depth estimation for colonoscopy | |
Gao et al. | Contour-aware network with class-wise convolutions for 3D abdominal multi-organ segmentation | |
Yang et al. | Scene-graph-driven semantic feature matching for monocular digestive endoscopy |