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Cognitive Computation
Published in:

13-06-2024 | Review

Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects

Authors: Abiy Abinet Mamo, Bealu Girma Gebresilassie, Aniruddha Mukherjee, Vikas Hassija, Vinay Chamola

Published in: Cognitive Computation | Issue 5/2024

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Abstract

In medical imaging, traditional methods have long been relied upon. However, the integration of Generative Adversarial Networks (GANs) has sparked a paradigm shift, ushering in a new era of innovation. Our comprehensive investigation explores the groundbreaking impact of GANs on medical imaging, examining the evolution from traditional techniques to GAN-driven approaches. Through meticulous analysis, we dissect various aspects of GANs, encompassing their taxonomy, historical progression, and diverse iterations such as Self-Attention GANs (SAGAN), Conditional GANs, and Progressive Growing GANs (PGGAN). Complemented by a practical case study, we scrutinize the extensive applications of GANs, spanning image generation, reconstruction, enhancement, segmentation, and super-resolution. Despite promising prospects, enduring challenges including data scarcity, interpretability issues, and ethical concerns persist. Looking ahead, we anticipate advancements in personalized and pathological image generation, cross-modal synthesis, real-time interactive image generation, and enhanced anomaly detection. Through this review, we underscore the transformative potential of GANs in reshaping medical imaging practices, while also outlining avenues for future research endeavors.
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Metadata
Title
Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects
Authors
Abiy Abinet Mamo
Bealu Girma Gebresilassie
Aniruddha Mukherjee
Vikas Hassija
Vinay Chamola
Publication date
13-06-2024
Publisher
Springer US
Published in
Cognitive Computation / Issue 5/2024
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-024-10291-3

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