| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Anees Abrol, Zening Fu, Mustafa Salman, Rogers Silva, Yuhui Du, Sergey Plis, Vince Calhou. Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nature communications. vol 12. issue 1. 2021-02-01. PMID:33441557. |
we conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural mri images and show the importance of representation learning for dl. |
2021-02-01 |
2023-08-13 |
human |
| Anees Abrol, Zening Fu, Mustafa Salman, Rogers Silva, Yuhui Du, Sergey Plis, Vince Calhou. Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nature communications. vol 12. issue 1. 2021-02-01. PMID:33441557. |
our findings highlight the presence of nonlinearities in neuroimaging data that dl can exploit to generate superior task-discriminative representations for characterizing the human brain. |
2021-02-01 |
2023-08-13 |
human |
| Eduardo B Mariottoni, Alessandro A Jammal, Carla N Urata, Samuel I Berchuck, Atalie C Thompson, Tais Estrela, Felipe A Medeiro. Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach. Scientific reports. vol 10. issue 1. 2020-11-17. PMID:31941958. |
training was done to predict rnfl thickness from raw unsegmented scans using conventional rnfl thickness measurements from good quality images as targets, forcing the dl algorithm to learn its own representation of rnfl. |
2020-11-17 |
2023-08-13 |
human |
| Catherine Hanson, Leyla Roskan Caglar, Stephen José Hanso. Attentional Bias in Human Category Learning: The Case of Deep Learning. Frontiers in psychology. vol 9. 2020-09-30. PMID:29706907. |
we conclude, after visualizing the hidden unit representations, that dl appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning. |
2020-09-30 |
2023-08-13 |
human |
| Awais Mansoor, Juan J Cerrolaza, Geovanny Perez, Elijah Biggs, Gustavo Nino, Marius George Lingurar. Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation. Proceedings of SPIE--the International Society for Optical Engineering. vol 10133. 2020-09-29. PMID:28592911. |
representation learning through deep learning (dl) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. |
2020-09-29 |
2023-08-13 |
Not clear |
| Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H Falk, Jocelyn Fauber. Deep learning-based electroencephalography analysis: a systematic review. Journal of neural engineering. vol 16. issue 5. 2020-08-24. PMID:31151119. |
recently, deep learning (dl) has shown great promise in helping make sense of eeg signals due to its capacity to learn good feature representations from raw data. |
2020-08-24 |
2023-08-13 |
Not clear |
| Igbe Tobore, Jingzhen Li, Liu Yuhang, Yousef Al-Handarish, Abhishek Kandwal, Zedong Nie, Lei Wan. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR mHealth and uHealth. vol 7. issue 8. 2020-08-24. PMID:31376272. |
the innovation of dl is a developing trend in the wake of big data for data representation and analysis. |
2020-08-24 |
2023-08-13 |
human |
| Ce Wang, Yi Qi, Guangcan Zh. Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China. Chemosphere. vol 257. 2020-07-30. PMID:32497840. |
in this study, we established four different deep learning (dl) models to capture inherent long-term dependencies in sequences and potential complex relationships among constituents by initiating with the original input into a representation at a higher abstract level. |
2020-07-30 |
2023-08-13 |
Not clear |
| Syed Jamal Safdar Gardezi, Ahmed Elazab, Baiying Lei, Tianfu Wan. Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review. Journal of medical Internet research. vol 21. issue 7. 2020-04-29. PMID:31350843. |
recent advancements of ml with deeper and extensive representation approaches, commonly known as deep learning (dl) approaches, have made a very significant impact on improving the diagnostics capabilities of the cad systems. |
2020-04-29 |
2023-08-13 |
Not clear |
| Yuko Nakamura, Toru Higaki, Fuminari Tatsugami, Yukiko Honda, Keigo Narita, Motonori Akagi, Kazuo Awa. Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality. Journal of computer assisted tomography. vol 44. issue 2. 2020-04-01. PMID:31789682. |
deep learning (dl), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. |
2020-04-01 |
2023-08-13 |
Not clear |
| Jiahuan Ren, Zhao Zhang, Sheng Li, Yang Wang, Guangcan Liu, Shuicheng Yan, Meng Wan. Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2020-01-16. PMID:31944974. |
in this paper, we investigate the robust dictionary learning (dl) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. |
2020-01-16 |
2023-08-13 |
Not clear |
| Jiahuan Ren, Zhao Zhang, Sheng Li, Yang Wang, Guangcan Liu, Shuicheng Yan, Meng Wan. Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2020-01-16. PMID:31944974. |
specifically, j-rfdl performs the robust representation by dl in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the dl process efficient. |
2020-01-16 |
2023-08-13 |
Not clear |
| Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, Shuicheng Ya. Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier. IEEE transactions on neural networks and learning systems. vol 29. issue 8. 2019-11-20. PMID:28922127. |
to obtain the representation coefficients, addl imposes a sparse -norm constraint on the coding coefficients instead of using or norm, since the - or -norm constraint applied in most existing dl criteria makes the training phase time consuming. |
2019-11-20 |
2023-08-13 |
Not clear |
| Wen-Kai Chen, Xiang-Yang Liu, Wei-Hai Fang, Pavlo O Dral, Ganglong Cu. Deep Learning for Nonadiabatic Excited-State Dynamics. The journal of physical chemistry letters. vol 9. issue 23. 2019-11-20. PMID:30403870. |
our dl is based on deep neural networks (dnns), which are used as accurate representations of the casscf ground- and excited-state potential energy surfaces (pess) of ch |
2019-11-20 |
2023-08-13 |
Not clear |
| Mohamed Rouis, Abdelkrim Ouafi, Salim Sba. Optimal level and order detection in wavelet decomposition for PCG signal denoising. Biomedizinische Technik. Biomedical engineering. vol 64. issue 2. 2019-10-15. PMID:29791308. |
discrete wavelet transform (dwt) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (dl). |
2019-10-15 |
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. |
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 |