| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| R Guerrero, C Qin, O Oktay, C Bowles, L Chen, R Joules, R Wolz, M C Valdés-Hernández, D A Dickie, J Wardlaw, D Ruecker. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NeuroImage. Clinical. vol 17. 2019-01-30. PMID:29527496. |
the proposed fully convolutional cnn architecture, called uresnet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. |
2019-01-30 |
2023-08-13 |
human |
| Jinzheng Cai, Le Lu, Zizhao Zhang, Fuyong Xing, Lin Yang, Qian Yi. Pancreas Segmentation in MRI using Graph-Based Decision Fusion on Convolutional Neural Networks. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. vol 9901. 2018-12-11. PMID:28083570. |
our approach conducts pancreatic detection and boundary segmentation with two types of cnn models respectively: 1) the tissue detection step to differentiate pancreas and non-pancreas tissue with spatial intensity context; 2) the boundary detection step to allocate the semantic boundaries of pancreas. |
2018-12-11 |
2023-08-13 |
Not clear |
| Hang Li, Xiu-Jun Gong, Hua Yu, Chang Zho. Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences. Molecules (Basel, Switzerland). vol 23. issue 8. 2018-11-05. PMID:30071670. |
the different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, cnn and lstm layers, respectively. |
2018-11-05 |
2023-08-13 |
human |
| Lei Zhang, Xiantong Zhen, Ling Shao, Jingkuan Son. Learning Match Kernels on Grassmann Manifolds for Action Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 1. 2018-09-24. PMID:30136940. |
specifically, we propose modeling actions as a linear subspace on the grassmann manifold; the subspace is a set of convolutional neural network (cnn) feature vectors pooled temporally over frames in semantic video clips, which simultaneously captures local discriminant patterns and temporal dynamics of motion. |
2018-09-24 |
2023-08-13 |
Not clear |
| Jufeng Yang, Jie Liang, Hui Shen, Kai Wang, Paul L Rosin, Ming-Hsuan Yan. Dynamic Match Kernel With Deep Convolutional Features for Image Retrieval. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 27. issue 11. 2018-07-31. PMID:29994213. |
although retrieved images are usually similar to a query in minutiae, they may be significantly different from a semantic perspective, which can be effectively distinguished by convolutional neural networks (cnn). |
2018-07-31 |
2023-08-13 |
Not clear |
| Mengmeng Zhang, Wei Li, Qian D. Diverse Region-Based CNN for Hyperspectral Image Classification. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 27. issue 6. 2018-07-30. PMID:29533899. |
in this paper, we propose a classification framework, called diverse region-based cnn, which can encode semantic context-aware representation to obtain promising features. |
2018-07-30 |
2023-08-13 |
Not clear |
| Mehedi Hasan, Alexander Kotov, April Carcone, Ming Dong, Sylvie Naar, Kathryn Brogan Hartlie. A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories. Journal of biomedical informatics. vol 62. 2018-02-12. PMID:27185608. |
we used a collection of motivational interview transcripts consisting of 11,353 utterances, which were manually annotated by two human coders as the gold standard, and experimented with state-of-art classifiers, including naïve bayes, j48 decision tree, support vector machine (svm), random forest (rf), adaboost, disclda, conditional random fields (crf) and convolutional neural network (cnn) in conjunction with lexical, contextual (label of the previous utterance) and semantic (distribution of words in the utterance across the linguistic inquiry and word count dictionaries) features. |
2018-02-12 |
2023-08-13 |
human |
| Haodi Li, Qingcai Chen, Buzhou Tang, Xiaolong Wang, Hua Xu, Baohua Wang, Dong Huan. CNN-based ranking for biomedical entity normalization. BMC bioinformatics. vol 18. issue Suppl 11. 2018-01-19. PMID:28984180. |
in this paper, we introduce a novel convolutional neural network (cnn) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. |
2018-01-19 |
2023-08-13 |
Not clear |
| Ashnil Kumar, Jinman Kim, David Lyndon, Michael Fulham, Dagan Fen. An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification. IEEE journal of biomedical and health informatics. vol 21. issue 1. 2017-11-16. PMID:28114041. |
we hypothesise that different cnn architectures learn different levels of semantic image representation and thus an ensemble of cnns will enable higher quality features to be extracted. |
2017-11-16 |
2023-08-13 |
Not clear |