| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Dae Hoe Kim, Seong Tae Kim, Jung Min Chang, Yong Man R. Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis. Physics in medicine and biology. vol 62. issue 3. 2017-10-19. PMID:28081006. |
first, 2d spatial image characteristics of dbt slices are encoded as a slice feature representation by convolutional neural network (cnn). |
2017-10-19 |
2023-08-13 |
Not clear |
| Dae Hoe Kim, Seong Tae Kim, Jung Min Chang, Yong Man R. Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis. Physics in medicine and biology. vol 62. issue 3. 2017-10-19. PMID:28081006. |
experimental results have demonstrated that the proposed latent feature representation achieves a higher level of classification performance in terms of receiver operating characteristic (roc) curves and the area under the roc curve values compared to performance with feature representation learned by conventional cnn and hand-crafted features. |
2017-10-19 |
2023-08-13 |
Not clear |
| Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koe. Protein-Ligand Scoring with Convolutional Neural Networks. Journal of chemical information and modeling. vol 57. issue 4. 2017-09-21. PMID:28368587. |
we describe convolutional neural network (cnn) scoring functions that take as input a comprehensive three-dimensional (3d) representation of a protein-ligand interaction. |
2017-09-21 |
2023-08-13 |
Not clear |
| No-Sang Kwak, Klaus-Robert Müller, Seong-Whan Le. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PloS one. vol 12. issue 2. 2017-08-17. PMID:28225827. |
a subsequent analysis inspects the representation found by the cnn at each layer and can thus contribute to a better understanding of the cnn's robust, accurate decoding abilities. |
2017-08-17 |
2023-08-13 |
Not clear |
| Tianshui Chen, Liang Lin, Lingbo Liu, Xiaonan Luo, Xuelong L. DISC: Deep Image Saliency Computing via Progressive Representation Learning. IEEE transactions on neural networks and learning systems. vol 27. issue 6. 2017-05-24. PMID:26742147. |
in particular, we model the image saliency from both the coarse-and fine-level observations, and utilize the deep convolutional neural network (cnn) to learn the saliency representation in a progressive manner. |
2017-05-24 |
2023-08-13 |
Not clear |
| Shi-Zhe Chen, Chun-Chao Guo, Jian-Huang La. Deep Ranking for Person Re-Identification via Joint Representation Learning. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 25. issue 5. 2016-12-16. PMID:27019494. |
the ranking model is solved with a deep convolutional neural network (cnn) that builds the relation between input image pairs and their similarity scores through joint representation learning directly from raw image pixels. |
2016-12-16 |
2023-08-13 |
Not clear |
| Hanxi Li, Yi Li, Fatih Porikl. DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 25. issue 4. 2016-07-20. PMID:26841390. |
in this paper, we present an efficient and very robust tracking algorithm using a single convolutional neural network (cnn) for learning effective feature representations of the target object in a purely online manner. |
2016-07-20 |
2023-08-13 |
Not clear |
| Jun Guo, Changhu Wang, Edgar Roman-Rangel, Hongyang Chao, Yong Ru. Building Hierarchical Representations for Oracle Character and Sketch Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 25. issue 1. 2016-03-18. PMID:26571529. |
the proposed representation is also complementary to convolutional neural network (cnn)-based models. |
2016-03-18 |
2023-08-13 |
Not clear |
| Sarita Thakoor, Javaan Chahl, M V Srinivasan, L Young, Frank Werblin, Butler Hine, Steven Zornetze. Bioinspired engineering of exploration systems for NASA and DoD. Artificial life. vol 8. issue 4. 2003-05-27. PMID:12650645. |
each of these representations can be efficiently modeled in semiconductor cellular nonlinear network (cnn) chips. |
2003-05-27 |
2023-08-12 |
human |