Results 1 to 10 of about 9,759,453 (201)

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN. [PDF]

PLoS ONE, 2021
Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especially ...
ChangHyuk Kwon, Sangjin Park, Soohyun Ko, Jaegyoon Ahn   +3 more
doaj   +3 more sources

Sample design, sample augmentation, and estimation for Wave 2 of the NSHAP. [PDF]

J Gerontol B Psychol Sci Soc Sci, 2014
The sample for the second wave (2010) of National Social Life, Health, and Aging Project (NSHAP) was designed to increase the scientific value of the Wave 1 (2005) data set by revisiting sample members 5 years after their initial interviews and augmenting this sample where possible.There were 2 important innovations.
O'Muircheartaigh C, English N, Pedlow S, Kwok PK.   +3 more
europepmc   +6 more sources

Improving Photometric Redshift Estimates with Training Sample Augmentation [PDF]

The Astrophysical Journal Letters
Large imaging surveys will rely on photometric redshifts (photo- z 's), which are typically estimated through machine-learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training sample for Legacy
Irene Moskowitz, Eric Gawiser, John Franklin Crenshaw, Brett H. Andrews, Alex I. Malz, Samuel Schmidt, The LSST Dark Energy Science Collaboration   +6 more
doaj   +2 more sources

Extended Subspace Projection Upon Sample Augmentation Based on Global Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. To address the earlier issues, the classification models such as subspace-based support vector machines, which have gained a ...
Jiaochan Hu, Xueji Shen, Haoyang Yu, Xiaodi Shang, Qiandong Guo, Bing Zhang   +5 more
doaj   +2 more sources

Dimensionality-modulated generative AI for safe biomedical dataset augmentation [PDF]

iScience
Summary: Generative AI can expand small biomedical datasets but may amplify noise and distort statistical relationships. We developed genESOM, a framework integrating an error control system into a generative AI method based on emergent self-organizing ...
Jörn Lötsch, André Himmelspach, Dario Kringel   +2 more
doaj   +2 more sources

Parkinson’s Disease Detection Based on Spectrogram-Deep Convolutional Generative Adversarial Network Sample Augmentation

IEEE Access, 2020
As an essential biological feature of human beings, voiceprint is increasingly used in medical research and diagnosis, especially in identifying Parkinson's Disease (PD). This paper proposes a Spectrogram Deep Convolutional Generative Adversarial Network
Zhi-Jing Xu, Rong-Fei Wang, Juan Wang, Da-Hai Yu   +3 more
doaj   +2 more sources

Sample Augmentation Method for Side-Scan Sonar Underwater Target Images Based on CBL-sinGAN

Journal of Marine Science and Engineering
The scarcity and difficulty in acquiring Side-scan sonar target images limit the application of deep learning algorithms in Side-scan sonar target detection.
Chengyang Peng, Shaohua Jin, Gang Bian, Yang Cui, Meina Wang   +4 more
doaj   +2 more sources

Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models [PDF]

BMC Medical Informatics and Decision Making
Background Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small.
Dan Liu, Samer El Kababji, Nicholas Mitsakakis, Lisa Pilgram, Thomas D. Walters, Mark Clemons, Gregory R. Pond, Alaa El-Hussuna, Khaled El Emam   +8 more
doaj   +2 more sources

Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [PDF]

Jisuanji kexue, 2022
Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the ...
XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo
doaj   +1 more source

Self-supervised Action Recognition Based on Skeleton Data Augmentation and Double Nearest Neighbor Retrieval [PDF]

Jisuanji kexue, 2023
Traditional self-supervised methods based on skeleton data often take different data augmentation of a sample as positive examples,and the rest of the samples are regarded as negative examples,which makes the ratio of positive and negative samples ...
WU Yushan, XU Zengmin, ZHANG Xuelian, WANG Tao
doaj   +1 more source