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Analysis of institutional authors

Rodríguez-álvarez, María JoséAuthor

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January 27, 2025
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Ultrasound breast images denoising using generative adversarial networks (GANs)

Publicated to: Intelligent Data Analysis. 28 (6): 1661-1678 - 2024-01-01 28(6), DOI: 10.3233/IDA-230631

Authors:

Jimenez-Gaona, Yuliana; Rodríguez-Álvarez, M.J.; Escudero, Lider ; Sandoval, Carlos ; Lakshminarayanan, Vasudevan
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Affiliations

Medihosp, Ave Eugenio Espejo & Shuaras 0739, Loja 50600, Ecuador - Author
Univ Politecn Valencia, Inst Instrumentac Imagen Mol I3M, Valencia, Spain - Author
Univ Tecn Particular Loja, Dept Quim & Ciencias Exactas, Loja, Ecuador - Author
Univ Waterloo, Dept Syst Design Engn Phys & Elect & Comp Engn, Waterloo, ON, Canada - Author
Univ Waterloo, Sch Optometry & Vis Sci, Theoret & Expt Epistemol Lab, Waterloo, ON, Canada - Author
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Abstract

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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Keywords

Breast cancerGenerative adversarial networGenerative adversarial networkUltrasound image denoising

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Intelligent Data Analysis, and although the journal is classified in the quartile Q4 (Agencia WoS (JCR)), its regional focus and specialization in Computer Science, Artificial Intelligence, give it significant recognition in a specific niche of scientific knowledge at an international level.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-02:

  • WoS: 2
  • Scopus: 3
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-02:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 28 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
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    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: Canada; Ecuador.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Jimenez-Gaona, Yuliana) .

    the author responsible for correspondence tasks has been Jimenez-Gaona, Yuliana.

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    Awards linked to the item

    Acknowledgments This project has been co-financed by the Spanish Government Grant Deepbreast PID2019-107790RB-C22 funded by MCIN/AEI/10.13039/501100011033.
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