| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Lei Zhang, Ji Liu, Bob Zhanga, David Zhangb, Ce Zh. Deep Cascade Model based Face Recognition: When Deep-layered Learning Meets Small Data. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-09-10. PMID:31502970. |
sparse representation based classification (src), nuclear-norm matrix regression (nmr), and deep learning (dl) have achieved a great success in face recognition (fr). |
2019-09-10 |
2023-08-13 |
Not clear |
| Lei Zhang, Ji Liu, Bob Zhanga, David Zhangb, Ce Zh. Deep Cascade Model based Face Recognition: When Deep-layered Learning Meets Small Data. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-09-10. PMID:31502970. |
dl, as a multi-step model, can learn powerful representation, but relies on large-scale data and computation resources for numerous parameters training with complicated back-propagation. |
2019-09-10 |
2023-08-13 |
Not clear |
| Jing Tang, Bao Yang, Yanhua Wang, Leslie Yin. Sparsity-constrained PET image reconstruction with learned dictionaries. Physics in medicine and biology. vol 61. issue 17. 2017-10-30. PMID:27494441. |
we propose to use dictionary learning (dl) based sparse signal representation in the formation of the prior for map pet image reconstruction. |
2017-10-30 |
2023-08-13 |
Not clear |
| Sandra Vieira, Walter H L Pinaya, Andrea Mechell. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience and biobehavioral reviews. vol 74. issue Pt A. 2017-10-27. PMID:28087243. |
dl differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecutive nonlinear transformations, achieving increasingly higher levels of abstraction and complexity. |
2017-10-27 |
2023-08-13 |
Not clear |
| Florin C Ghesu, Edward Krubasik, Bogdan Georgescu, Vivek Singh, Yefeng Zheng, Joachim Hornegger, Dorin Comanici. Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing. IEEE transactions on medical imaging. vol 35. issue 5. 2017-06-28. PMID:27046846. |
to our knowledge, this is the first successful demonstration of the dl potential to detection and segmentation in full 3d data with parametrized representations. |
2017-06-28 |
2023-08-13 |
Not clear |
| Anh-Tuan Trinh, Erik Harvey-Girard, Fellipe Teixeira, Leonard Male. Cryptic laminar and columnar organization in the dorsolateral pallium of a weakly electric fish. The Journal of comparative neurology. vol 524. issue 2. 2016-09-16. PMID:26234725. |
the architecture of dl and the expansive representation of its input, taken together with the strong expression of n-methyl-d-aspartate (nmda) receptors by its cells, are consistent with theoretical ideas concerning the cortical computations of pattern separation and memory storage via bump attractors. |
2016-09-16 |
2023-08-13 |
Not clear |
| Andrew Janowczyk, Anant Madabhush. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of pathology informatics. vol 7. 2016-08-26. PMID:27563488. |
deep learning (dl) is a representation learning approach ideally suited for image analysis challenges in digital pathology (dp). |
2016-08-26 |
2023-08-13 |
Not clear |
| Jiexiong Tang, Chenwei Deng, Guang-Bin Huan. Extreme Learning Machine for Multilayer Perceptron. IEEE transactions on neural networks and learning systems. vol 27. issue 4. 2016-07-20. PMID:25966483. |
by doing so, it achieves more compact and meaningful feature representations than the original elm; 2) by exploiting the advantages of elm random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (dl), the hidden layers of the proposed framework are trained in a forward manner. |
2016-07-20 |
2023-08-13 |
Not clear |
| Vasileios S Charisis, Leontios J Hadjileontiadi. Use of adaptive hybrid filtering process in Crohn's disease lesion detection from real capsule endoscopy videos. Healthcare technology letters. vol 3. issue 1. 2016-05-25. PMID:27222730. |
more specifically, this scheme is based on: (i) a hybrid adaptive filtering (haf) process, that utilises genetic algorithms to the curvelet-based representation of images for efficient extraction of the lesion-related morphological characteristics, (ii) differential lacunarity (dl) analysis for texture feature extraction from the haf-filtered images and (iii) support vector machines for robust classification performance. |
2016-05-25 |
2023-08-13 |
Not clear |
| Matthias Samwald, Jose Antonio Miñarro Giménez, Richard D Boyce, Robert R Freimuth, Klaus-Peter Adlassnig, Michel Dumontie. Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies. BMC medical informatics and decision making. vol 15. 2016-03-29. PMID:25880555. |
pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on owl 2 dl ontologies. |
2016-03-29 |
2023-08-13 |
Not clear |
| Liping Jing, Michael K Ng, Tieyong Zen. Dictionary learning-based subspace structure identification in spectral clustering. IEEE transactions on neural networks and learning systems. vol 24. issue 8. 2015-03-30. PMID:24808560. |
in this paper, we study dictionary learning (dl) approach to identify the representation of low-dimensional subspaces from high-dimensional and nonnegative data. |
2015-03-30 |
2023-08-13 |
Not clear |
| Jian Cheng, Tianzi Jiang, Rachid Deriche, Dinggang Shen, Pew-Thian Ya. Regularized spherical polar fourier diffusion MRI with optimal dictionary learning. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. vol 16. issue Pt 1. 2014-02-27. PMID:24505721. |
in diffusion mri (dmri), cs methods proposed for reconstruction of diffusion-weighted signal and the ensemble average propagator (eap) utilize two kinds of dictionary learning (dl) methods: 1) discrete representation dl (dr-dl), and 2) continuous representation dl (cr-dl). |
2014-02-27 |
2023-08-12 |
Not clear |
| Yang Chen, Xindao Yin, Luyao Shi, Huazhong Shu, Limin Luo, Jean-Louis Coatrieux, Christine Toumouli. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing. Physics in medicine and biology. vol 58. issue 16. 2014-02-20. PMID:23917704. |
stemming from sparse representation theory, the proposed patch-based dl approach allows effective suppression of both mottled noise and streak artifacts. |
2014-02-20 |
2023-08-12 |
Not clear |
| Tomasz Adamusiak, Olivier Bodenreide. Quality assurance in LOINC using Description Logic. AMIA ... Annual Symposium proceedings. AMIA Symposium. vol 2012. 2013-07-30. PMID:23304386. |
to assess whether errors can be found in loinc by changing its representation to owl dl and comparing its classification to that of snomed ct. |
2013-07-30 |
2023-08-12 |
Not clear |
| Stefan Schulz, Elena Beisswanger, László van den Hoek, Olivier Bodenreider, Erik M van Mullige. Alignment of the UMLS semantic network with BioTop: methodology and assessment. Bioinformatics (Oxford, England). vol 25. issue 12. 2009-07-21. PMID:19478019. |
in contrast to the sn, it is founded upon strict ontological principles, using owl dl as a formal representation language, which has become standard in the semantic web. |
2009-07-21 |
2023-08-12 |
Not clear |
| Daniel L Rubin, Olivier Dameron, Mark A Muse. Use of description logic classification to reason about consequences of penetrating injuries. AMIA ... Annual Symposium proceedings. AMIA Symposium. 2007-02-15. PMID:16779120. |
we hypothesize that such symbolic knowledge can be modeled using ontologies, and that the reasoning task can be accomplished using knowl-edge representation in description logics (dl) and automatic classification. |
2007-02-15 |
2023-08-12 |
Not clear |
| Songmao Zhang, Olivier Bodenreider, Christine Golbreic. Experience in reasoning with the foundational model of anatomy in OWL DL. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2007-01-03. PMID:17094240. |
we converted it from its frame-based representation in protégé into owl dl. |
2007-01-03 |
2023-08-12 |
Not clear |
| Eveline A Crone, Carter Wendelken, Sarah Donohue, Linda van Leijenhorst, Silvia A Bung. Neurocognitive development of the ability to manipulate information in working memory. Proceedings of the National Academy of Sciences of the United States of America. vol 103. issue 24. 2006-09-11. PMID:16738055. |
these results indicate that increased recruitment of right dl pfc and bilateral parietal cortex during adolescence is associated with improvements in the ability to work with object representations. |
2006-09-11 |
2023-08-12 |
human |
| V N Kazakov, T I Panova, V F Andreeva, E D Krakhotkina, N I Shevchenko, L E Panov. [Projections of the dorsal raphe nucleus and midbrain central gray substance to the cat limbic system]. Morfologiia (Saint Petersburg, Russia). vol 124. issue 4. 2004-04-15. PMID:14628552. |
as far as septum, amigdala, hippocampus and cingular cortex are concerned, it was found impossible to refer them to any of these systems--either nociceptive or antinociceptive--basing solely on the findings of morphological studies because of approximately similar representation of axons of neurons in vl sgc, dl sgc, rd in these structures. |
2004-04-15 |
2023-08-12 |
cat |
| K A Spackman, K E Campbel. Compositional concept representation using SNOMED: towards further convergence of clinical terminologies. Proceedings. AMIA Symposium. 1999-03-16. PMID:9929317. |
the dl model has many advantages: it establishes a formal semantics for snomed assertions and suggests a syntax; it provides a basis for understanding expressiveness and computational complexity, through correspondence with known results from dls; and it helps to clarify the relationships among existing concept representation methods in snomed, nhs clinical terms (formerly the read codes), and galen, making a path to convergence more clear. |
1999-03-16 |
2023-08-12 |
Not clear |