Synthetic CT image generation of shape-controlled lung cancer using semi-conditional InfoGAN and its applicability for type classificationLung cancerCT imagingImage synthesisClassificationCNNGANIn recent years, convolutional neural network (CNN), an artificial intelligence technology with superior image ...
0.01, ***P < 0.001. Statistical analysis inawas performed with two-tailed Mann–Whitney test and inc,ewith unpaired two-tailedttest. Data are shown as mean ± S.E.M. Source data are provided in the Source Data file. Full size image proSP-C mistrafficking does not contribute...
Existing methods often struggle with limited training data, impacting segmentation accuracy. This study addresses this challenge by proposing a novel conditional generative adversarial network (CGAN) approach. Building on the U-Net architecture, the CGAN generates synthetic scene images resembling real ...
cloud removal; cGAN; custom loss function; image-to-image; synthetic images; SAR to optical image translation; crop type mapping; remote sensing1. Introduction To ensure global food security, which is one of the seventeen sustainable development goals defined by the United Nations to be ...
[21] in the BigGAN architecture to allow for the generation of synthetic data of arbitrary size in the image domain. Adversarial training between c D ( X ; θ d , θ E d ) 𝑐𝐷(𝑋;𝜃𝑑,𝜃𝐸𝑑) and c G ( Z ; θ g , θ E g ) 𝑐𝐺(𝑍;𝜃𝑔,𝜃𝐸...
cloud removal; cGAN; custom loss function; image-to-image; synthetic images; SAR to optical image translation; crop type mapping; remote sensing1. Introduction To ensure global food security, which is one of the seventeen sustainable development goals defined by the United Nations to be ...
Additionally, to address the extreme class imbalance in the Bot-IoT dataset, this approach proposes using synthetic data generation as a solution. The output of this stage is 𝑥𝑎𝑑𝑣xadv, which is used as input for the next stage. The workflow of the proposed approach can be ...
Moreover, their lower resolution (1 pixel = 30 m) due to the far view of the sensor poses challenges for accurate synthetic image generation. Thus, the heterogeneous patterns in satellite imagery require the capture of diverse features and the learning of a greater number of data distributions ...
the training dataset should undergo data augmentation using traditional image transformation techniques and Generative Adversarial Networks (GANs) to prevent class imbalance issues that may lead to model overfitting. In this study, we investigate the feasibility of creating dermoscopic images that have a ...
2.1. Data Generation Module 2.1.1. TomoSAR Principle As shown in Figure 3, traditional synthetic aperture radars project the three-dimensional spatial distribution of scatterers along the elevation direction to the two-dimensional azimuth-range plane, which causes the elevation distribution of scatterers...