Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea.
Korean J Radiol. 2023 Jul;24(7):698-714. doi: 10.3348/kjr.2022.0765.
In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.
近年来，人工智能，特别是计算机视觉中的基于目标检测的深度学习，在计算能力的发展和图形处理器单元的广泛使用的推动下取得了重大进展。基于目标检测的深度学习技术已应用于各个领域，包括医学成像领域，在疾病检测方面已经取得了显著的成果。然而，深度学习的应用并不总是能保证令人满意的性能，研究人员一直在通过反复试验来识别导致性能下降的因素并改进他们的模型。此外，由于黑盒问题，人类无法理解深度学习网络的中间过程；因此，识别性能不佳的深度学习模型中的问题可能具有挑战性。本文强调了医学成像领域中深度学习的每个步骤中可能导致性能下降的潜在问题，并讨论了必须考虑的因素以提高深度学习模型的性能。希望开始进行深度学习研究的研究人员可以通过了解本研究中讨论的问题来减少所需的反复试验次数。
