Conditional Generative Adversarial Networks for Solving Heat Transfer Problems

Martinez, Matthew; Heiner, Olivia

doi:10.2172/1673172

Cited by 6 publications

(2 citation statements)

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“…Thus, according to the values given in Table 4, the CGAN is consistent with a higher quality image [63] and achieves success in ultrasound denoising images compared to the WGAN. This can be attributed to the fact that CGAN uses the Unet architecture as the generator model and Binary Cross Entropy (BCE) as the loss function (in addition to the L1 loss) [67,68] 5.…”

Section: Discussionmentioning

confidence: 99%

Ultrasound breast images denoising using generative adversarial networks (GANs)

Jiménez-Gaona,

Rodríguez-Alvarez,

Escudero

et al. 2024

IDA

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INTRODUCTION: Ultrasound in conjunction with mammography imaging, plays a vital role in the early detection and diagnosis of breast cancer. However, speckle noise affects medical ultrasound images and degrades visual radiological interpretation. Speckle carries information about the interactions of the ultrasound pulse with the tissue microstructure, which generally causes several difficulties in identifying malignant and benign regions. The application of deep learning in image denoising has gained more attention in recent years. OBJECTIVES: The main objective of this work is to reduce speckle noise while preserving features and details in breast ultrasound images using GAN models. METHODS: We proposed two GANs models (Conditional GAN and Wasserstein GAN) for speckle-denoising public breast ultrasound databases: BUSI, DATASET A, AND UDIAT (DATASET B). The Conditional GAN model was trained using the Unet architecture, and the WGAN model was trained using the Resnet architecture. The image quality results in both algorithms were measured by Peak Signal to Noise Ratio (PSNR, 35–40 dB) and Structural Similarity Index (SSIM, 0.90–0.95) standard values. RESULTS: The experimental analysis clearly shows that the Conditional GAN model achieves better breast ultrasound despeckling performance over the datasets in terms of PSNR = 38.18 dB and SSIM = 0.96 with respect to the WGAN model (PSNR = 33.0068 dB and SSIM = 0.91) on the small ultrasound training datasets. CONCLUSIONS: The observed performance differences between CGAN and WGAN will help to better implement new tasks in a computer-aided detection/diagnosis (CAD) system. In future work, these data can be used as CAD input training for image classification, reducing overfitting and improving the performance and accuracy of deep convolutional algorithms.

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Section: Discussionmentioning

confidence: 99%

Ultrasound breast images denoising using generative adversarial networks (GANs)

Jiménez-Gaona,

Rodríguez-Alvarez,

Escudero

et al. 2024

IDA

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“…These networks provide a way to learn deep representations without requiring any extensively annotated training data. GAN models are able to learn the representations by deriving back-propagation signals through a competitive process involving a pair of networks (Martinez & Heiner, 2020).…”

Section: Hydrological Generative Adversarial Network (Hydro-gan)mentioning

confidence: 99%

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Filali Boubrahimi,

Neema,

Nassar

et al. 2024

Water Resources Research

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With increasing demands for precise water resource management, there is a growing need for advanced techniques in mapping water bodies. The currently deployed satellites provide complementary data that are either of high spatial or high temporal resolutions. As a result, there is a clear trade‐off between space and time when considering a single data source. For the efficient monitoring of multiple environmental resources, various Earth science applications need data at high spatial and temporal resolutions. To address this need, many data fusion methods have been described in the literature, that rely on combining data snapshots from multiple sources. Traditional methods face limitations due to sensitivity to atmospheric disturbances and other environmental factors, resulting in noise, outliers, and missing data. This paper introduces Hydrological Generative Adversarial Network (Hydro‐GAN), a novel machine learning‐based method that utilizes modified GANs to enhance boundary accuracy when mapping low‐resolution MODIS data to high‐resolution Landsat‐8 images. We propose a new non‐saturating loss function for the Hydro‐GAN generator, which maximizes the log of discriminator probabilities to promote stable updates and aid convergence. By focusing on reducing squared differences between real and synthetic images, our approach enhances training stability and overall performance. We specifically focus on mapping water bodies using MODIS and Landsat‐8 imagery due to their relevance in water resource management tasks. Our experimental results demonstrate the effectiveness of Hydro‐GAN in generating high‐resolution water body maps, outperforming traditional methods in terms of boundary accuracy and overall quality.

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