Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations,...

Area of Science:

• Computer Vision
• Digital Pathology
• Medical Imaging

Background:

• Generative adversarial networks (GANs) are advanced AI models for realistic image synthesis.
• GANs comprise a generator and discriminator trained adversarially.
• Their success in computer vision suggests potential in medical diagnostics.

Purpose of the Study:

• To review the emerging applications of GANs in digital histopathology.
• To examine the potential benefits and challenges of using GANs in this field.
• To highlight ethical considerations associated with synthetic medical images.

Main Methods:

• Review of current literature on GANs in digital histopathology.
• Analysis of GAN applications including image synthesis and enhancement.
• Discussion of ethical implications and limitations.

Main Results:

• GANs show promise for generating realistic microscopic images, aiding in rare disease visualization and learning.
• Applications include image enhancement techniques like color normalization, virtual staining, and artifact removal.
• Ethical concerns regarding synthetic data, bias, privacy, and accountability are identified.

Conclusions:

• GANs offer significant potential for transforming digital pathology, particularly for preprocessing enhancements.
• Careful evaluation of data quality, bias, privacy, and transparency is essential.
• Developing clear regulations is imperative for the ethical application of GANs in histopathology.