| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Wenhui Zhang, Surajit Ra. From coarse to fine: a deep 3D probability volume contours framework for tumour segmentation and dose painting in PET images. Frontiers in radiology. vol 3. 2023-09-25. PMID:37745205. |
to address these challenges, we introduce a 3d coarse-to-fine framework, integrating a cnn with a kernel smoothing-based probability volume contour approach (kspc). |
2023-09-25 |
2023-10-07 |
Not clear |
| Yihao Liu, Lianrui Zuo, Shuo Han, Yuan Xue, Jerry L Prince, Aaron Caras. Coordinate Translator for Learning Deformable Medical Image Registration. Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. vol 13594. 2023-01-30. PMID:36716114. |
we then propose a novel deformable registration network, im2grid, that uses multiple coordinate translator's with the hierarchical features extracted from a cnn encoder and outputs a deformation field in a coarse-to-fine fashion. |
2023-01-30 |
2023-08-14 |
Not clear |
| Di Wang, Yue Pan, Oguz C Durumeric, Joseph M Reinhardt, Eric A Hoffman, Joyce D Schroeder, Gary E Christense. PLOSL: Population learning followed by one shot learning pulmonary image registration using tissue volume preserving and vesselness constraints. Medical image analysis. vol 79. 2022-04-17. PMID:35430476. |
a coarse-to-fine convolution encoder-decoder cnn architecture is used to register large and small shape features. |
2022-04-17 |
2023-08-13 |
Not clear |
| Lev Kiar Avberšek, Astrid Zeman, Hans Op de Beec. Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision. Journal of vision. vol 21. issue 10. 2021-11-24. PMID:34533580. |
we compared cnn performance after standard and coarse-to-fine training with a wide range of datasets from behavioral and neuroimaging experiments. |
2021-11-24 |
2023-08-13 |
human |
| Shanggang Lin, Lianwen Jin, Ziwei Che. Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments. Sensors (Basel, Switzerland). vol 21. issue 18. 2021-10-01. PMID:34577433. |
we use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (cnn) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. |
2021-10-01 |
2023-08-13 |
Not clear |
| Zhi-Song Liu, Wan-Chi Siu, Yui-Lam Cha. Features Guided Face Super-Resolution via Hybrid Model of Deep Learning and Random Forests. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 30. 2021-09-06. PMID:33819156. |
in this paper, we combine new techniques in convolutional neural network and random forests to a hierarchical cnn based random forests (hcrf) approach for face super-resolution in a coarse-to-fine manner. |
2021-09-06 |
2023-08-13 |
Not clear |
| Lei Wang, Han Liu, Yaling Lu, Hang Chen, Jian Zhang, Jiantao P. A coarse-to-fine deep learning framework for optic disc segmentation in fundus images. Biomedical signal processing and control. vol 51. 2021-04-17. PMID:33850515. |
in this study, we proposed a coarse-to-fine deep learning framework on the basis of a classical convolutional neural network (cnn), known as the u-net model, to accurately identify the optic disc. |
2021-04-17 |
2023-08-13 |
Not clear |
| Chaochen Ma, Qing Ren, Jian Zha. Optical-numerical method based on a convolutional neural network for full-field subpixel displacement measurements. Optics express. vol 29. issue 6. 2021-04-06. PMID:33820347. |
the basic idea of the method is to compare images of an object decorated with speckle patterns before and after deformation by cnn, and thereby to achieve a coarse-to-fine subpixel displacement estimation. |
2021-04-06 |
2023-08-13 |
Not clear |
| Liangjun Chen, Zhengwang Wu, Dan Hu, Ya Wang, Zhanhao Mo, Li Wang, Weili Lin, Dinggang Shen, Gang L. A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. vol 12267. 2021-02-12. PMID:33564753. |
in this paper, we propose a spatial context guided, coarse-to-fine deep convolutional neural network (cnn) based framework for accurate infant subcortical segmentation. |
2021-02-12 |
2023-08-13 |
Not clear |
| Yang Wen, Bin Sheng, Ping Li, Weiyao Lin, David Dagan Fen. Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:30296229. |
second, we introduce a coarse-to-fine cnn to learn different sizes of filter kernels. |
2019-11-20 |
2023-08-13 |
Not clear |
| Yang Wen, Bin Sheng, Ping Li, Weiyao Lin, David Dagan Fen. Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:30296229. |
in this paper, we introduce a novel deep color guided coarse-to-fine convolutional neural network (cnn) framework to address this problem. |
2019-11-20 |
2023-08-13 |
Not clear |
| Yang You, Cewu Lu, Weiming Wang, Chi-Keung Tan. Relative CNN-RNN: Learning Relative Atmospheric Visibility From Images. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 1. 2018-09-24. PMID:30028702. |
the cnn-rnn model makes use of shortcut connections to bridge a cnn module and an rnn coarse-to-fine module. |
2018-09-24 |
2023-08-13 |
human |
| Yang You, Cewu Lu, Weiming Wang, Chi-Keung Tan. Relative CNN-RNN: Learning Relative Atmospheric Visibility From Images. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 1. 2018-09-24. PMID:30028702. |
the relative cnn-rnn coarse-to-fine model, where cnn stands for convolutional neural network and rnn stands for recurrent neural network, exploits the joint power of relative support vector machine, which has a good ranking representation, and the data-driven deep learning features derived from our novel cnn-rnn model. |
2018-09-24 |
2023-08-13 |
human |