Kim Min Ji, Kim Sang Hoon, Kim Suk Min, Nam Ji Hyung, Hwang Young Bae, Lim Yun Jeong
Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Republic of Korea.
Department of Intelligent Systems and Robotics, College of Electrical & Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.
Diagnostics (Basel). 2023 Sep 22;13(19):3023. doi: 10.3390/diagnostics13193023.
Artificial intelligence (AI) is a subfield of computer science that aims to implement computer systems that perform tasks that generally require human learning, reasoning, and perceptual abilities. AI is widely used in the medical field. The interpretation of medical images requires considerable effort, time, and skill. AI-aided interpretations, such as automated abnormal lesion detection and image classification, are promising areas of AI. However, when images with different characteristics are extracted, depending on the manufacturer and imaging environment, a so-called domain shift problem occurs in which the developed AI has a poor versatility. Domain adaptation is used to address this problem. Domain adaptation is a tool that generates a newly converted image which is suitable for other domains. It has also shown promise in reducing the differences in appearance among the images collected from different devices. Domain adaptation is expected to improve the reading accuracy of AI for heterogeneous image distributions in gastrointestinal (GI) endoscopy and medical image analyses. In this paper, we review the history and basic characteristics of domain shift and domain adaptation. We also address their use in gastrointestinal endoscopy and the medical field more generally through published examples, perspectives, and future directions.
人工智能（AI）是计算机科学的一个子领域，旨在实现能够执行通常需要人类学习、推理和感知能力的任务的计算机系统。人工智能在医学领域有着广泛应用。医学图像的解读需要大量精力、时间和技能。人工智能辅助解读，如自动异常病变检测和图像分类，是人工智能很有前景的领域。然而，当根据制造商和成像环境提取具有不同特征的图像时，就会出现所谓的领域转移问题，即所开发的人工智能通用性较差。领域自适应被用于解决这一问题。领域自适应是一种生成适合其他领域的新转换图像的工具。它在减少从不同设备收集的图像之间的外观差异方面也显示出前景。领域自适应有望提高人工智能在胃肠道（GI）内窥镜检查和医学图像分析中对异质图像分布的读取准确性。在本文中，我们回顾了领域转移和领域自适应的历史及基本特征。我们还通过已发表的实例、观点和未来方向，更全面地探讨它们在胃肠道内窥镜检查和医学领域中的应用。
