适应(眼睛)
计算机科学
领域(数学分析)
人工智能
领域(数学)
域适应
医学影像学
图像处理
图像(数学)
机器学习
计算机视觉
心理学
数学分析
数学
神经科学
分类器(UML)
纯数学
作者
Min Ji Kim,Sang Hoon Kim,Suk Min Kim,Ji Hyung Nam,Youngbae Hwang,Yun Jeong Lim
出处
期刊:Diagnostics
[MDPI AG]
日期:2023-09-22
卷期号:13 (19): 3023-3023
被引量:1
标识
DOI:10.3390/diagnostics13193023
摘要
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.
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