| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Lu Liu, Jelmer M Wolterink, Christoph Brune, Raymond N J Veldhui. Anatomy-aided deep learning for medical image segmentation: a review. Physics in medicine and biology. vol 66. issue 11. 2021-11-25. PMID:33906186. |
in this paper, we provide a review of anatomy-aided dl for medical image segmentation which covers systematically summarized anatomical information categories and corresponding representation methods. |
2021-11-25 |
2023-08-13 |
Not clear |
| Yangsong Zhang, Huan Cai, Li Nie, Peng Xu, Sirui Zhao, Cuntai Gua. An end-to-end 3D convolutional neural network for decoding attentive mental state. Neural networks : the official journal of the International Neural Network Society. vol 144. 2021-11-24. PMID:34492547. |
inspired by the deep learning (dl) methods in the research of brain-computer interface (bci) field, a 3d representation of eeg signal was introduced into attention detection task, and a 3d convolutional neural network model with cascade and parallel convolution operations was proposed. |
2021-11-24 |
2023-08-13 |
human |
| Marianne Defresne, Sophie Barbe, Thomas Schie. Protein Design with Deep Learning. International journal of molecular sciences. vol 22. issue 21. 2021-11-18. PMID:34769173. |
as no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated dl architecture for design and related tasks. |
2021-11-18 |
2023-08-13 |
Not clear |
| Md Rezaul Karim, Oya Beyan, Achille Zappa, Ivan G Costa, Dietrich Rebholz-Schuhmann, Michael Cochez, Stefan Decke. Deep learning-based clustering approaches for bioinformatics. Briefings in bioinformatics. vol 22. issue 1. 2021-11-12. PMID:32008043. |
in contrast, deep learning (dl)-based representation and feature learning for clustering have not been reviewed and employed extensively. |
2021-11-12 |
2023-08-13 |
Not clear |
| Jingyan Xu, Frédéric No. Convex optimization algorithms in medical image reconstruction - in the age of AI. Physics in medicine and biology. 2021-11-10. PMID:34757943. |
we also discuss how convexity can be employed to improve the generalizability and representation power of dl networks in general. |
2021-11-10 |
2023-08-13 |
Not clear |
| Natalya F Noy, Daniel L Rubi. Translating the Foundational Model of Anatomy into OWL. Web semantics (Online). vol 6. issue 2. 2021-10-20. PMID:18688289. |
our complete representation of the fma in owl consists of two components: an owl dl component that contains the fma constructs that are compatible with owl dl; and an owl full component that imports the owl dl component and adds the fma constructs that owl dl does not allow. |
2021-10-20 |
2023-08-12 |
human |
| Natalya F Noy, Sherri de Coronado, Harold Solbrig, Gilberto Fragoso, Frank W Hartel, Mark A Muse. Representing the NCI Thesaurus in OWL DL: Modeling tools help modeling languages. Applied ontology. vol 3. issue 3. 2021-10-20. PMID:19789731. |
we have studied the requirements for knowledge representation of the nci thesaurus, and considered how owl dl (and its implementation in protégé-owl) satisfies these requirements. |
2021-10-20 |
2023-08-12 |
Not clear |
| Natalya F Noy, Sherri de Coronado, Harold Solbrig, Gilberto Fragoso, Frank W Hartel, Mark A Muse. Representing the NCI Thesaurus in OWL DL: Modeling tools help modeling languages. Applied ontology. vol 3. issue 3. 2021-10-20. PMID:19789731. |
in this paper, we discuss the areas where owl dl was sufficient for representing required components, where tool support that would hide some of the complexity and extra levels of indirection would be required, and where language expressiveness is not sufficient given the representation requirements. |
2021-10-20 |
2023-08-12 |
Not clear |
| Jintae Kim, Sera Park, Dongbo Min, Wankyu Ki. Comprehensive Survey of Recent Drug Discovery Using Deep Learning. International journal of molecular sciences. vol 22. issue 18. 2021-10-19. PMID:34576146. |
in addition, we introduce a comprehensive summary of a variety of drug and protein representations, dl models, and commonly used benchmark datasets or tools for model training and testing. |
2021-10-19 |
2023-08-13 |
Not clear |
| Tahjid Ashfaque Mostafa, Sara Soltaninejad, Tara L McIsaac, Irene Chen. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. Sensors (Basel, Switzerland). vol 21. issue 19. 2021-10-14. PMID:34640763. |
we also propose three ensemble network architectures that combine all the time frequency representations and dl architectures. |
2021-10-14 |
2023-08-13 |
Not clear |
| Yan Zhu, Lihong Liu, Bo Gao, Jing Liu, Xingchao Qiao, Chaojie Lian, Yongqun H. TCDO: A Community-Based Ontology for Integrative Representation and Analysis of Traditional Chinese Drugs and Their Properties. Evidence-based complementary and alternative medicine : eCAM. vol 2021. 2021-10-05. PMID:34603473. |
the logical tcd representation in tcdo supports computer-assisted reasoning and queries using tools such as description logic (dl) and sparql queries. |
2021-10-05 |
2023-08-13 |
Not clear |
| Youjun Xu, Chenjing Cai, Shiwei Wang, Luhua Lai, Jianfeng Pe. Efficient molecular encoders for virtual screening. Drug discovery today. Technologies. vol 32-33. 2021-09-06. PMID:33386090. |
in this review, we present an overview of two-dimensional-, three-dimensional-, and dl-based molecular encoders, summarize recent progress of vs using dl technologies, and propose a general framework of dl molecular encoder-based vs. perspectives on the future directions of molecular representations and applications in the prediction of active compounds are also provided. |
2021-09-06 |
2023-08-13 |
Not clear |
| Kaustav Bera, Ian Katz, Anant Madabhush. Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO clinical cancer informatics. vol 4. 2021-08-31. PMID:33166198. |
these patterns are typically categorized as either (1) handcrafted, which involve domain-inspired attributes, such as nuclear shape, or (2) deep learning (dl)-based representations, which tend to be more abstract. |
2021-08-31 |
2023-08-13 |
Not clear |
| Walid Hariri, Ali Nari. Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review. Soft computing. 2021-08-31. PMID:34456618. |
the computed tomography of the chest and x-ray images gives a rich representation of the patient's lung that is less time-consuming and allows an efficient viral pneumonia detection using the dl algorithms. |
2021-08-31 |
2023-08-13 |
human |
| Mirjam Quaak, Laurens van de Mortel, Rajat Mani Thomas, Guido van Winge. Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis. NeuroImage. Clinical. vol 30. 2021-07-30. PMID:33677240. |
however, it is not yet used to its full potential: most studies use pre-engineered features, whereas one of the main advantages of dl is its ability to learn representations of minimally processed data. |
2021-07-30 |
2023-08-13 |
Not clear |
| Jens Kleesiek, Benedikt Kersjes, Kai Ueltzhöffer, Jacob M Murray, Carsten Rother, Ullrich Köthe, Heinz-Peter Schlemme. Discovering Digital Tumor Signatures-Using Latent Code Representations to Manipulate and Classify Liver Lesions. Cancers. vol 13. issue 13. 2021-07-11. PMID:34206336. |
modern generative deep learning (dl) architectures allow for unsupervised learning of latent representations that can be exploited in several downstream tasks. |
2021-07-11 |
2023-08-13 |
Not clear |
| Wonjun Ko, Eunjin Jeon, Seungwoo Jeong, Jaeun Phyo, Heung-Il Su. A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces. Frontiers in human neuroscience. vol 15. 2021-06-19. PMID:34140883. |
recently, deep learning (dl) has had a theoretical/practical impact on bci research because of its use in learning representations of complex patterns inherent in eeg. |
2021-06-19 |
2023-08-13 |
Not clear |
| Saurabh Kumar Srivastava, Sandeep Kumar Singh, Jasjit S Sur. State-of-the-art methods in healthcare text classification system: AI paradigm. Frontiers in bioscience (Landmark edition). vol 25. 2021-06-14. PMID:31585909. |
this paper emphasizes text representations and its linage with ml, dl, and rl approaches, which is an important marker for intelligence segregation. |
2021-06-14 |
2023-08-13 |
Not clear |
| Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan X. Deep learning in digital pathology image analysis: a survey. Frontiers of medicine. vol 14. issue 4. 2021-04-27. PMID:32728875. |
traditional methods usually require hand-crafted domain-specific features, and dl methods can learn representations without manually designed features. |
2021-04-27 |
2023-08-13 |
Not clear |
| 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. |
however, their conclusions are often based on pre-engineered features depriving dl of its main advantage - representation learning. |
2021-02-01 |
2023-08-13 |
human |