| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Yuan Luo, Yu Cheng, Özlem Uzuner, Peter Szolovits, Justin Starre. Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes. Journal of the American Medical Informatics Association : JAMIA. vol 25. issue 1. 2019-01-02. PMID:29025149. |
unlike typical cnn models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. |
2019-01-02 |
2023-08-13 |
Not clear |
| Jiaxing Ye, Shunya Ito, Nobuyuki Toyam. Computerized Ultrasonic Imaging Inspection: From Shallow to Deep Learning. Sensors (Basel, Switzerland). vol 18. issue 11. 2018-11-08. PMID:30405086. |
using the dataset, we performed a comprehensive experimental comparison of various computer vision techniques, including both conventional methods using hand-crafted visual features and the most recent convolutional neural networks (cnn) which generate multiple-layer stacking for representation learning. |
2018-11-08 |
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 |
| Gong Cheng, Junwei Han, Peicheng Zhou, Dong X. Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 28. issue 1. 2018-09-24. PMID:30235112. |
specifically, the first regularizer enforces the cnn feature representations of the training samples before and after rotation to be mapped closely to each other in order to achieve rotation-invariance. |
2018-09-24 |
2023-08-13 |
Not clear |
| Wufeng Xue, Gary Brahm, Sachin Pandey, Stephanie Leung, Shuo L. Full left ventricle quantification via deep multitask relationships learning. Medical image analysis. vol 43. 2018-08-28. PMID:28987903. |
the proposed dmtrl first obtains expressive and robust cardiac representations with a deep convolution neural network (cnn); then models the temporal dynamics of cardiac sequences effectively with two parallel recurrent neural network (rnn) modules. |
2018-08-28 |
2023-08-13 |
human |
| Wufeng Xue, Gary Brahm, Sachin Pandey, Stephanie Leung, Shuo L. Full left ventricle quantification via deep multitask relationships learning. Medical image analysis. vol 43. 2018-08-28. PMID:28987903. |
the cnn representation, rnn temporal modeling, bayesian multitask relationship learning, and softmax classifier establish an effective and integrated network which can be learned in an end-to-end manner. |
2018-08-28 |
2023-08-13 |
human |
| Xiuli Li, Hao Zhang, Xiaolu Zhang, Hao Liu, Guotong Xi. Exploring transfer learning for gastrointestinal bleeding detection on small-size imbalanced endoscopy images. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2017. 2018-08-08. PMID:29060286. |
the success of convolutional neural network (cnn) is attributed to their ability to learn rich midlevel image representations as opposed to hand-crafted low-level features used in many natural image classification methods. |
2018-08-08 |
2023-08-13 |
Not clear |
| Xiuli Li, Hao Zhang, Xiaolu Zhang, Hao Liu, Guotong Xi. Exploring transfer learning for gastrointestinal bleeding detection on small-size imbalanced endoscopy images. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2017. 2018-08-08. PMID:29060286. |
in this paper, we explored transfer learning for gastrointestinal bleeding detection on small-size imbalanced endoscopy images, and showed how image representations learned with cnn on large-scale annotated datasets can be efficiently transferred to other tasks with limited amount of training data. |
2018-08-08 |
2023-08-13 |
Not clear |
| Eduardo Carabez, Miho Sugi, Isao Nambu, Yasuhiro Wad. Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces. Computational intelligence and neuroscience. vol 2017. 2018-08-03. PMID:29250108. |
the cnn models are given a novel single trial three-dimensional (3d) representation of the eeg data as an input, maintaining temporal and spatial information as close to the experimental setup as possible, a relevant characteristic as eliciting p300 has been shown to cause stronger activity in certain brain regions. |
2018-08-03 |
2023-08-13 |
human |
| Dongyu Zhang, Liang Lin, Tianshui Chen, Xian Wu, Wenwei Tan, Ebroul Izquierd. Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 26. issue 1. 2018-07-30. PMID:27831874. |
then, we utilize a branched fully cnn for learning structural and textural representations, respectively. |
2018-07-30 |
2023-08-13 |
Not clear |
| Yinzuo Zhou, Luming Zhang, Chao Zhang, Ping Li, Xuelong L. Perceptually Aware Image Retargeting for Mobile Devices. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 27. issue 5. 2018-07-30. PMID:29451495. |
afterward, an aggregation-based cnn is developed to hierarchically learn the deep representation for each gsp. |
2018-07-30 |
2023-08-13 |
human |
| 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 |
| Angel Cruz-Roa, Hannah Gilmore, Ajay Basavanhally, Michael Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski, Anant Madabhushi, Fabio Gonzále. High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PloS one. vol 13. issue 5. 2018-07-30. PMID:29795581. |
convolutional neural network (cnn) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. |
2018-07-30 |
2023-08-13 |
Not clear |
| Jing Li, Tao Qiu, Chang Wen, Kai Xie, Fang-Qing We. Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion. Sensors (Basel, Switzerland). vol 18. issue 7. 2018-07-02. PMID:29958478. |
c2d-cnn combines the features learnt from the original pixels with the image representation learnt by cnn, and then makes decision-level fusion, which can significantly improve the performance of face recognition. |
2018-07-02 |
2023-08-13 |
Not clear |
| Dan Liu, Xuejun Liu, Yiguang W. Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model. Sensors (Basel, Switzerland). vol 18. issue 5. 2018-06-12. PMID:29695129. |
a deep cnn network is firstly used to automatically learn a hierarchical feature representation of the image. |
2018-06-12 |
2023-08-13 |
Not clear |
| Jin Li, Min Zhang, Danshi Wang, Shaojun Wu, Yueying Zha. Joint atmospheric turbulence detection and adaptive demodulation technique using the CNN for the OAM-FSO communication. Optics express. vol 26. issue 8. 2018-06-08. PMID:29715985. |
compared to previous approaches using the self-organizing mapping (som), deep neural network (dnn) and other cnns, the proposed cnn achieves the highest atda and ada due to the advanced multi-layer representation learning without feature extractors designed carefully by numerous experts. |
2018-06-08 |
2023-08-13 |
Not clear |
| Matthew C Chen, Robyn L Ball, Lingyao Yang, Nathaniel Moradzadeh, Brian E Chapman, David B Larson, Curtis P Langlotz, Timothy J Amrhein, Matthew P Lungre. Deep Learning to Classify Radiology Free-Text Reports. Radiology. vol 286. issue 3. 2018-04-26. PMID:29135365. |
classification of performance of a cnn model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application pefinder. |
2018-04-26 |
2023-08-13 |
human |
| Sebastian Gehrmann, Franck Dernoncourt, Yeran Li, Eric T Carlson, Joy T Wu, Jonathan Welt, John Foote, Edward T Moseley, David W Grant, Patrick D Tyler, Leo A Cel. Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives. PloS one. vol 13. issue 2. 2018-04-06. PMID:29447188. |
convolutional neural networks (cnn) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. |
2018-04-06 |
2023-08-13 |
Not clear |
| Tianyu Tang, Shilin Zhou, Zhipeng Deng, Huanxin Zou, Lin Le. Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining. Sensors (Basel, Switzerland). vol 17. issue 2. 2018-02-14. PMID:28208587. |
recently, due to the powerful feature representations, region convolutional neural networks (cnn) based detection methods have achieved state-of-the-art performance in computer vision, especially faster r-cnn. |
2018-02-14 |
2023-08-13 |
Not clear |
| Sang-Il Oh, Hang-Bong Kan. Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems. Sensors (Basel, Switzerland). vol 17. issue 1. 2018-02-12. PMID:28117742. |
the unary classifiers for the two sensors are the cnn with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. |
2018-02-12 |
2023-08-13 |
Not clear |