Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions.
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Abstract
Generative Adversarial Networks (GANs) have gained significant attention in the field of image synthesis, particularly in computer vision. GANs consist of a generative model and a discriminative model, trained in an adversarial setting to generate realistic and novel data. In the context of image synthesis, the generator produces synthetic images while the discriminator determines their authenticity by comparing them to real examples. Through iterative training, the generator allows the creation of images that are indistinguishable from real ones, leading to high-quality image generation. Considering their success in computer vision, GANs hold great potential for medical diagnostic applications. In the medical field, GANs can generate images of rare diseases, aid in learning, and be utilized as visualization tools. Importantly, the full potential of GANs in the medical domain is now being explored. GANs can leverage unlabeled medical images, which are large in size, numerous in quantity, and challenging to annotate manually. GANs have demonstrated remarkable capabilities in image synthesis and have the potential to significantly impact digital histopathology. This review article focuses on the emerging use of GANs in digital histopathology, examining their applications and potential challenges. Histopathology plays a crucial role in disease diagnosis, and GANs can contribute by generating realistic microscopic images. However, ethical considerations arise due to the reliance on synthetic or pseudo-generated images. Therefore, the manuscript also explores the current limitations and highlights the ethical considerations associated with the use of this technology. In conclusion, GANs offer significant potential in transforming digital pathology when applied to specific and narrow tasks (preprocessing enhancements). Digital histopathology has seen an emerging use of GANs for image enhancement such as color (stain) normalization, virtual staining, and ink/marker removal. Evaluating data quality, addressing biases, protecting privacy, ensuring accountability and transparency, and developing regulation are imperative to ensure the ethical application of GANs.Copyright © 2023. Published by Elsevier Inc.