Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, Maryland; Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia; Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, Maryland.
Department of Oral Diagnostic Sciences and Research, School of Dentistry, Meharry Medical College, Nashville, Tennessee.
Mod Pathol. 2024 Jan;37(1):100369. doi: 10.1016/j.modpat.2023.100369. Epub 2023 Oct 27.
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, whereas the discriminator determines their authenticity by comparing them with 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 used as visualization tools. 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 because of the reliance on synthetic or pseudogenerated images. Therefore, the manuscript also explores the current limitations and highlights the ethical considerations associated with the use of this technology. In conclusion, digital histopathology has seen an emerging use of GANs for image enhancement, such as color (stain) normalization, virtual staining, and ink/marker removal. GANs offer significant potential in transforming digital pathology when applied to specific and narrow tasks (preprocessing enhancements). Evaluating data quality, addressing biases, protecting privacy, ensuring accountability and transparency, and developing regulation are imperative to ensure the ethical application of GANs.
生成对抗网络 (GAN) 在图像合成领域，特别是计算机视觉领域，引起了广泛关注。GAN 由生成模型和判别模型组成，它们在对抗性环境中进行训练，以生成逼真和新颖的数据。在图像合成的上下文中，生成器生成合成图像，而判别器通过将其与真实示例进行比较来确定其真实性。通过迭代训练，生成器允许创建与真实图像难以区分的图像，从而实现高质量的图像生成。鉴于它们在计算机视觉方面的成功，GAN 在医学诊断应用方面具有巨大潜力。在医学领域，GAN 可以生成罕见疾病的图像，辅助学习，并用作可视化工具。GAN 可以利用未标记的医学图像，这些图像尺寸大、数量多，手动注释具有挑战性。GAN 在图像合成方面表现出卓越的能力，并有可能对数字组织病理学产生重大影响。本文重点介绍了 GAN 在数字组织病理学中的新兴应用，探讨了它们的应用和潜在挑战。组织病理学在疾病诊断中起着至关重要的作用，GAN 可以通过生成逼真的微观图像做出贡献。然而，由于依赖于合成或伪生成的图像，因此会出现伦理问题。因此，本文还探讨了当前的局限性，并强调了与使用这项技术相关的伦理问题。总之，数字组织病理学已经开始将 GAN 用于图像增强，例如颜色（染色）归一化、虚拟染色和墨水/标记去除。当应用于特定和狭窄的任务（预处理增强）时，GAN 在转化数字病理学方面具有巨大潜力。评估数据质量、解决偏差、保护隐私、确保问责制和透明度以及制定法规对于确保 GAN 的伦理应用至关重要。
