Results 71 to 80 of about 598,648 (280)

MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

Applied Sciences, 2022
Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets
Binbin Shi, Lijuan Zhang, Jie Huang, Huilin Zheng, Jian Wan, Lei Zhang   +5 more
doaj   +1 more source

The Data Augmentation Algorithm

The data augmentation (DA) algorithms are popular Markov chain Monte Carlo (MCMC) algorithms often used for sampling from intractable probability distributions. This review article comprehensively surveys DA MCMC algorithms, highlighting their theoretical foundations, methodological implementations, and diverse applications in frequentist and Bayesian ...
Roy, Vivekananda, Khare, Kshitij, Hobert, James P.   +2 more
openaire   +2 more sources

Data Augmentation for Conversational AI

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023
Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages.
Heydar Soudani, Evangelos Kanoulas, Faegheh Hasibi   +2 more
openaire   +3 more sources

The cooperative regulation of miR‐221 by APE1 and AUF1 impacts p27Kip1 defining a miR signature relevant for cervical cancer

FEBS Open Bio, EarlyView.
A regulatory axis involving APE1, AUF1, and miR‐221 is proposed. Pri‐miR‐221 is processed by DROSHA and DICER to generate mature miR‐221, which targets p27Kip1 mRNA. APE1 and AUF1 compete for pre‐miR‐221 binding. Reduced APE1/AUF1 levels impair miR‐221 biogenesis, decrease p27Kip1 mRNA degradation, and promote cell cycle progression, chemoresistance ...
Matilde Clarissa Malfatti, Nicolò Gualandi, Gianluca Tell, Giulia Antoniali   +3 more
wiley   +1 more source

Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation

Algorithms, 2022
Data augmentation methods enrich datasets with augmented data to improve the performance of neural networks. Recently, automated data augmentation methods have emerged, which automatically design augmentation strategies.
Misgana Negassi, Diane Wagner, Alexander Reiterer   +2 more
doaj   +1 more source

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

, 2019
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to ...
Cai, Shaofan, Ding, Yifan, Fan, Haoqiang, Huang, Haibin, Liu, Jiaming, Wang, Chuan, Wang, Jue, Wang, Yuzhi, Wu, Chi-Hao, Xu, Qin, Zhou, Yuqian   +10 more
core   +1 more source

Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study

FEBS Open Bio, EarlyView.
In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem   +32 more
wiley   +1 more source

Comparison of Different Image Data Augmentation Approaches

Journal of Imaging, 2021
Convolutional neural networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small ...
Loris Nanni, Michelangelo Paci, Sheryl Brahnam, Alessandra Lumini   +3 more
doaj   +1 more source

Gibbs Max-margin Topic Models with Data Augmentation [PDF]

, 2013
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic ...
Chen, Ning, Perkins, Hugh, Zhang, Bo, Zhu, Jun   +3 more
core  

Efficient data augmentation techniques for some classes of state space models

, 2022
Data augmentation improves the convergence of iterative algorithms, such as the EM algorithm and Gibbs sampler by introducing carefully designed latent variables.
Tan, Linda S. L.
core