| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Tao Zheng, Yimei Gao, Fei Wang, Chenhao Fan, Xingzhi Fu, Mei Li, Ya Zhang, Shaodian Zhang, Handong M. Detection of medical text semantic similarity based on convolutional neural network. BMC medical informatics and decision making. vol 19. issue 1. 2020-02-26. PMID:31391038. |
in this paper, we propose a convolutional neural network (cnn) based method which can better utilize semantic information contained in report texts to accelerate the retrieving process. |
2020-02-26 |
2023-08-13 |
Not clear |
| Di Lin, Ruimao Zhang, Yuanfeng Ji, Ping Li, Hui Huan. SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images. IEEE transactions on cybernetics. vol 50. issue 3. 2020-02-20. PMID:30582564. |
while deep convolutional neural networks (cnns) have been successful in solving semantic segmentation, we encounter the problem of optimizing cnn training for the informative context using depth data to enhance the segmentation accuracy. |
2020-02-20 |
2023-08-13 |
Not clear |
| Rahul Paul, Matthew Schabath, Yoganand Balagurunathan, Ying Liu, Qian Li, Robert Gillies, Lawrence O Hall, Dmitry B Goldgo. Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features. Tomography (Ann Arbor, Mich.). vol 5. issue 1. 2020-02-03. PMID:30854457. |
we discovered that 26 deep features from the vgg-s neural network and 12 deep features from our trained cnn could be explained by semantic or traditional quantitative features. |
2020-02-03 |
2023-08-13 |
Not clear |
| Jiemin Zhai, Huiqi L. An Improved Full Convolutional Network Combined with Conditional Random Fields for Brain MR Image Segmentation Algorithm and its 3D Visualization Analysis. Journal of medical systems. vol 43. issue 9. 2020-01-29. PMID:31338693. |
firstly, we extract semantic information by cnn with the attention module and get the coarse segmentation results through a specific pixel-level classifier. |
2020-01-29 |
2023-08-13 |
Not clear |
| Chanjun Chun, Seung-Ki Ry. Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning. Sensors (Basel, Switzerland). vol 19. issue 24. 2019-12-17. PMID:31842513. |
moreover, the cnn model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. |
2019-12-17 |
2023-08-13 |
Not clear |
| Sheng Guo, Weilin Huang, Limin Wang, Yu Qia. Locally Supervised Deep Hybrid Model for Scene Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 26. issue 2. 2019-11-20. PMID:28113936. |
the deep features obtained at the top fully connected layer of the cnn (fc-features) exhibit rich global semantic information and are extremely effective in image classification. |
2019-11-20 |
2023-08-13 |
Not clear |
| Umar Asif, Mohammed Bennamoun, Ferdous A Sohe. A Multi-Modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling. IEEE transactions on pattern analysis and machine intelligence. vol 40. issue 9. 2019-11-20. PMID:28866483. |
this is achieved through three postulates: 1) spatial invariance $-$ this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations, rotations, and scale changes, 2) high discriminative capability $-$ this is achieved by introducing fisher encoding within the cnn architecture to learn features which have small inter-class similarities and large intra-class compactness, and 3) multi-modal hierarchical fusion$-$ this is achieved through the regularization of semantic segmentation to a multi-modal cnn architecture, where class probabilities are estimated at different hierarchical levels (i.e., image- and pixel-levels), and fused into a conditional random field (crf)-based inference hypothesis, the optimization of which produces consistent class labels in rgb-d images. |
2019-11-20 |
2023-08-13 |
Not clear |
| Xiao Xie, Xiwen Cai, Junpei Zhou, Nan Cao, Yingcai W. A Semantic-Based Method for Visualizing Large Image Collections. IEEE transactions on visualization and computer graphics. vol 25. issue 7. 2019-11-20. PMID:29993720. |
the semantic information extractor employs an image captioning technique based on convolutional neural network (cnn) to produce descriptive captions for images, which can be transformed into semantic keywords. |
2019-11-20 |
2023-08-13 |
Not clear |
| Yalong Jiang, Zheru Ch. A CNN Model for Semantic Person Part Segmentation with Capacity Optimization. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:30571629. |
a cnn model for semantic person part segmentation with capacity optimization. |
2019-11-20 |
2023-08-13 |
Not clear |
| Carolina Redondo-Cabrera, Marcos Baptista-Rios, Roberto J Lopez-Sastr. Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:30802862. |
training a convolutional neural network (cnn) for semantic segmentation typically requires to collect a large amount of accurate pixel-level annotations, a hard and expensive task. |
2019-11-20 |
2023-08-13 |
Not clear |
| Zitian Cheny, Yanwei Fuy, Yinda Zhang, Yu-Gang Jiang, Xiangyang Xue, Leonid Siga. Multi-level Semantic Feature Augmentation for One-shot Learning. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:30969924. |
the encoder part of the trinet learns to map multi-layer visual features from cnn to a semantic vector. |
2019-11-20 |
2023-08-13 |
Not clear |
| Zhongliang Yang, Yongfeng Huang, Yiran Jiang, Yuxi Sun, Yu-Jin Zhang, Pengcheng Lu. Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network. Scientific reports. vol 8. issue 1. 2019-11-04. PMID:29679019. |
in this study, we present a clinical intelligent decision approach based on convolutional neural networks(cnn), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. |
2019-11-04 |
2023-08-13 |
Not clear |
| Jingwen Ye, Yongcheng Jing, Xinchao Wang, Kairi Ou, Dacheng Tao, Mingli Son. Edge-Sensitive Human Cutout with Hierarchical Granularity and Loopy Matting Guidance. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-09-19. PMID:31502968. |
our model comprises two parts, the extensible hierarchical semantic segmentation block using cnn and the matting module composed of guided filters. |
2019-09-19 |
2023-08-13 |
human |
| Jingwen Ye, Yongcheng Jing, Xinchao Wang, Kairi Ou, Dacheng Tao, Mingli Son. Edge-Sensitive Human Cutout with Hierarchical Granularity and Loopy Matting Guidance. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-09-19. PMID:31502968. |
the obtained matting map is then in turn fed back to the cnn in the first block for refining the semantic segmentation results. |
2019-09-19 |
2023-08-13 |
human |
| Bolei Zhou, David Bau, Aude Oliva, Antonio Torralb. Interpreting Deep Visual Representations via Network Dissection. IEEE transactions on pattern analysis and machine intelligence. vol 41. issue 9. 2019-09-11. PMID:30040625. |
the proposed method quantifies the interpretability of cnn representations by evaluating the alignment between individual hidden units and visual semantic concepts. |
2019-09-11 |
2023-08-13 |
Not clear |
| Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Ruecker. DRINet for Medical Image Segmentation. IEEE transactions on medical imaging. vol 37. issue 11. 2019-08-19. PMID:29993738. |
the u-net architecture is one of the most well-known cnn architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. |
2019-08-19 |
2023-08-13 |
Not clear |
| Yaojun Dai, Shiju Yan, Bin Zheng, Chengli Son. Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification. Physics in medicine and biology. vol 63. issue 24. 2019-08-01. PMID:30524071. |
image attributes of the nodules, as human-nameable high-level semantic labels, are rarely used to build a convolutional neural network (cnn). |
2019-08-01 |
2023-08-13 |
Not clear |
| Yiling Wu, Shuhui Wang, Guoli Song, Qingming Huan. Online Asymmetric Metric Learning With Multi-Layer Similarity Aggregation for Cross-Modal Retrieval. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 9. 2019-07-03. PMID:30951466. |
considering that multiple cnn feature layers naturally represent visual information from low-level visual patterns to high-level semantic abstraction, we propose a new asymmetric image-text similarity formulation which aggregates the layer-wise visual-textual similarities parameterized by different bilinear parameter matrices. |
2019-07-03 |
2023-08-13 |
Not clear |
| Davis M Vigneault, Weidi Xie, Carolyn Y Ho, David A Bluemke, J Alison Nobl. Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks. Medical image analysis. vol 48. 2019-06-06. PMID:29857330. |
here, we present Ω-net (omega-net): a novel convolutional neural network (cnn) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. |
2019-06-06 |
2023-08-13 |
human |
| Mingliang Fu, Weijia Zho. DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation. Sensors (Basel, Switzerland). vol 19. issue 5. 2019-03-04. PMID:30823451. |
for the latter, intermediate cues, such as 3d object coordinates, semantic keypoints, or virtual control points instead of pose parameters are regressed by cnn in the first stage. |
2019-03-04 |
2023-08-13 |
Not clear |