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

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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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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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  1. School of Computer Engineering KIIT, Bhubaneshwar, 751024, India

    Abiy Abinet Mamo, Bealu Girma Gebresilassie, Aniruddha Mukherjee & Vikas Hassija

  2. Department of Electrical and Electronics Engineering, BITS-Pilani, Pilani Campus, Rajasthan, 333031, India

    Vinay Chamola

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This work was carried out in close collaboration between all co-authors. They first defined the research theme and contributed an early map of the review, further they refined the map, and wrote the paper. All authors have contributed to, seen, and approved the final manuscript.

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Mamo, A.A., Gebresilassie, B.G., Mukherjee, A. et al. Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects. Cogn Comput (2024). https://doi.org/10.1007/s12559-024-10291-3

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