| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Eric W Prince, Debashis Ghosh, Carsten Görg, Todd C Hankinso. Uncertainty-Aware Deep Learning Classification of Adamantinomatous Craniopharyngioma from Preoperative MRI. Diagnostics (Basel, Switzerland). vol 13. issue 6. 2023-03-29. PMID:36980440. |
classification using deep learning (dl) provides a solution to support a non-invasive diagnosis of acp through neuroimaging, but it is still limited in implementation, a major reason being the lack of predictive uncertainty representation. |
2023-03-29 |
2023-08-14 |
Not clear |
| Fatemeh Sadeghi, Ata Larijani, Omid Rostami, Diego Martín, Parisa Hajirahim. A Novel Multi-Objective Binary Chimp Optimization Algorithm for Optimal Feature Selection: Application of Deep-Learning-Based Approaches for SAR Image Classification. Sensors (Basel, Switzerland). vol 23. issue 3. 2023-02-11. PMID:36772219. |
with the maturity of dl tools, many data-driven polarimetric synthetic aperture radar (polsar) representation models have been suggested, most of which are based on deep convolutional neural networks (dcnns). |
2023-02-11 |
2023-08-14 |
Not clear |
| Vincent Dong, Duriye Damla Sevgi, Sudeshna Sil Kar, Sunil K Srivastava, Justis P Ehlers, Anant Madabhush. Evaluating the utility of deep learning for predicting therapeutic response in diabetic eye disease. Frontiers in ophthalmology. vol 2. 2023-02-06. PMID:36744216. |
deep learning (dl) is a technique explored within ophthalmology that requires large datasets to distinguish feature representations with high diagnostic performance. |
2023-02-06 |
2023-08-14 |
Not clear |
| Wen-Feng Shen, He-Wei Tang, Jia-Bo Li, Xiang Li, Si Che. Multimodal data fusion for supervised learning-based identification of USP7 inhibitors: a systematic comparison. Journal of cheminformatics. vol 15. issue 1. 2023-01-11. PMID:36631899. |
two deep learning (dl) models and nine classical machine learning (ml) models were then constructed based on different combinations of the above molecular representations under three activity cutoff values, and a total of 15 groups of experiments (75 experiments) were implemented. |
2023-01-11 |
2023-08-14 |
Not clear |
| Omneya Attalla. RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics. Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society. vol 233. 2023-01-09. PMID:36619376. |
then, for each dl model, deep features are obtained, and their dimensions are decreased using the fast walsh hadamard transform, yielding a time-frequency representation of the covid-19 patterns. |
2023-01-09 |
2023-08-14 |
Not clear |
| Kritika Gaur, Miheer M Jagta. Role of Artificial Intelligence and Machine Learning in Prediction, Diagnosis, and Prognosis of Cancer. Cureus. vol 14. issue 11. 2022-12-07. PMID:36475188. |
a bigger family of machine learning techniques built on artificial neural networks and representation learning is deep learning (dl). to clarify, we require ai, ml, and dl to predict cancer risk, survival chances, cancer recurrence, cancer diagnosis, and cancer prognosis. |
2022-12-07 |
2023-08-14 |
Not clear |
| Stefanos Tsimenidis, Eleni Vrochidou, George A Papakosta. Omics Data and Data Representations for Deep Learning-Based Predictive Modeling. International journal of molecular sciences. vol 23. issue 20. 2022-10-27. PMID:36293133. |
this work provides an overview of the most common types of biological data and data representations that are used to train dl models, with additional information on the models themselves and the various tasks that are being tackled. |
2022-10-27 |
2023-08-14 |
Not clear |
| Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew Blanchard, Massimiliano Lupo Pasin. Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. Journal of cheminformatics. vol 14. issue 1. 2022-10-17. PMID:36253845. |
graph convolutional neural network (gcnn) is a popular class of deep learning (dl) models in material science to predict material properties from the graph representation of molecular structures. |
2022-10-17 |
2023-08-14 |
Not clear |
| Armin W Thomas, Christopher Ré, Russell A Poldrac. Interpreting mental state decoding with deep learning models. Trends in cognitive sciences. vol 26. issue 11. 2022-10-12. PMID:36223760. |
deep learning (dl) models are highly promising for mental state decoding because of their unmatched ability to learn versatile representations of complex data. |
2022-10-12 |
2023-08-14 |
Not clear |
| Elham Beheshtian, Kristin Putman, Samantha M Santomartino, Vishwa S Parekh, Paul H Y. Generalizability and Bias in a Deep Learning Pediatric Bone Age Prediction Model Using Hand Radiographs. Radiology. 2022-09-27. PMID:36165796. |
the dl model was tested from september 2021 to december 2021 on an internal validation set and an external test set of pediatric hand radiographs with diverse demographic representation. |
2022-09-27 |
2023-08-14 |
Not clear |
| Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhan. Deep learning methods for molecular representation and property prediction. Drug discovery today. 2022-09-27. PMID:36167282. |
in this review, we summarize contemporary applications of deep learning (dl) methods for molecular representation and property prediction. |
2022-09-27 |
2023-08-14 |
Not clear |
| Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhan. Deep learning methods for molecular representation and property prediction. Drug discovery today. 2022-09-27. PMID:36167282. |
we also highlight the challenges and opportunities of dl methods for molecular representation and property prediction. |
2022-09-27 |
2023-08-14 |
Not clear |
| Muhammad Waqas Nadeem, Hock Guan Goh, Muzammil Hussain, Soung-Yue Liew, Ivan Andonovic, Muhammad Adnan Kha. Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions. Sensors (Basel, Switzerland). vol 22. issue 18. 2022-09-23. PMID:36146130. |
deep learning (dl) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. |
2022-09-23 |
2023-08-14 |
Not clear |
| Xiaomeng Zheng, Bogdan Dumitrescu, Jiamou Liu, Ciprian Doru Giurcănean. Multivariate Time Series Imputation: An Approach Based on Dictionary Learning. Entropy (Basel, Switzerland). vol 24. issue 8. 2022-08-26. PMID:36010721. |
the problem addressed by dictionary learning (dl) is the representation of data as a sparse linear combination of columns of a matrix called dictionary. |
2022-08-26 |
2023-08-14 |
Not clear |
| Delora Baptista, João Correia, Bruno Pereira, Miguel Roch. Evaluating molecular representations in machine learning models for drug response prediction and interpretability. Journal of integrative bioinformatics. 2022-08-26. PMID:36017668. |
however, in recent years, end-to-end deep learning (dl) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. |
2022-08-26 |
2023-08-14 |
Not clear |
| Hongyi Gu, Burhaneddin Yaman, Steen Moeller, Jutta Ellermann, Kamil Ugurbil, Mehmet Akçakay. Revisiting [Formula: see text]-wavelet compressed-sensing MRI in the era of deep learning. Proceedings of the National Academy of Sciences of the United States of America. vol 119. issue 33. 2022-08-08. PMID:35939712. |
using ideas such as algorithm unrolling and advanced optimization methods over large databases that dl algorithms utilize, along with conventional insights from wavelet representations and cs theory, we show that [formula: see text]-wavelet cs can be fine-tuned to a level close to dl reconstruction for accelerated mri. |
2022-08-08 |
2023-08-14 |
Not clear |
| Sergio J Sanabria, Amir M Pirmoazen, Jeremy Dahl, Aya Kamaya, Ahmed El Kaffa. Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic Steatosis. Ultrasound in medicine & biology. 2022-08-01. PMID:35914993. |
the aim of this work was to compare dl classification scores for liver steatosis using different data representations constructed from raw us data. |
2022-08-01 |
2023-08-14 |
Not clear |
| Wei Tang, Fazhi He, Yu Liu, Yansong Dua. MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2022-07-28. PMID:35901003. |
with powerful feature representation ability, deep learning (dl)-based methods have improved such fusion results but still have not achieved satisfactory performance. |
2022-07-28 |
2023-08-14 |
Not clear |
| Tarek Berghout, Mohamed Benbouzid, Toufik Bentrcia, Yassine Amirat, Leïla-Hayet Mous. Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis. Entropy (Basel, Switzerland). vol 24. issue 7. 2022-07-27. PMID:35885232. |
in this specific topic, health deterioration modeling with deep learning (dl) is the widely studied representation learning tool due to its adaptation ability to rapid changes in data complexity and drift. |
2022-07-27 |
2023-08-14 |
Not clear |
| Tarek Berghout, Mohamed Benbouzid, Toufik Bentrcia, Yassine Amirat, Leïla-Hayet Mous. Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis. Entropy (Basel, Switzerland). vol 24. issue 7. 2022-07-27. PMID:35885232. |
in this context, the present paper proposes an investigation of further deeper representations by exposing dl models themselves to recurrent expansion with multiple repeats. |
2022-07-27 |
2023-08-14 |
Not clear |