Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

Liu, Yuanyuan; Liu, Xin; Dong, Fang; Wang, Chang; Wang, Zihong

doi:10.1016/j.eng.2018.11.018

Cited by 121 publications

(51 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nie et al [64] generated synthetic pelvic CT images using GANs. Liu et al [69] synthesized HCC samples using an approach based on a generative adversarial network (GAN) combined with a deep neural network. Han et al proposed [70] a two-step GAN-based DA to generate and refine brain magnetic resonance (MR) images with/without tumors separately.…”

Section: Related Workmentioning

confidence: 99%

GAN-based synthetic brain PET image generation

2020

View full text Add to dashboard Cite

In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed model can create brain PET images for three different stages of Alzheimer's disease-normal control (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD).

show abstract

Section: Related Workmentioning

confidence: 99%

GAN-based synthetic brain PET image generation

2020

View full text Add to dashboard Cite

show abstract

“…The issue of sample making is gaining increasing attention from scholars [48]. Some researchers have proposed a method of combining unsupervised learning and semisupervised learning to make samples of each tree species using sparse autoencoders and deep belief networks when testing organic carbon content [49]. It simplifies the production of samples.…”

Section: Impact Of Label Samples On Classificationmentioning

confidence: 99%

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

2020

View full text Add to dashboard Cite

Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achieve end-to-end classification. Therefore, a tree classification method based on deep learning is proposed in this study. This method combines the semantic segmentation network U-Net and the feature extraction network ResNet into an improved Res-UNet network, where the convolutional layer of the U-Net network is represented by the residual unit of ResNet, and linear interpolation is used instead of deconvolution in each upsampling layer. At the output of the network, conditional random fields are used for post-processing. This network model is used to perform classification experiments on airborne orthophotos of Nanning Gaofeng Forest Farm in Guangxi, China. The results are then compared with those of U-Net and ResNet networks. The proposed method exhibits higher classification accuracy with an overall classification accuracy of 87%. Thus, the proposed model can effectively implement forest tree species classification and provide new opportunities for tree species classification in southern China.

show abstract

“…The work in [31] proposed a method based on a generative adversarial network put together with a deep neural network. The generative adversarial network was trained with the training group to generate virtual sample data, which increased the training group.…”

Section: Virtual Sample Generation Techniquementioning

confidence: 99%

A Novel Virtual Sample Generation Method to Overcome the Small Sample Size Problem in Computer Aided Medical Diagnosing

2019

View full text Add to dashboard Cite

Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%–95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples.

show abstract

Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

Cited by 121 publications

References 50 publications

GAN-based synthetic brain PET image generation

GAN-based synthetic brain PET image generation

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

A Novel Virtual Sample Generation Method to Overcome the Small Sample Size Problem in Computer Aided Medical Diagnosing

Contact Info

Product

Resources

About