Unsupervised Domain Adaptation Using Adversarial Learning and Maximum Square Loss for Liver Tumors Detection in Multi-phase CT Images.
Unsupervised Domain Adaptation Using Adversarial Learning and Maximum Square Loss for Liver Tumors Detection in Multi-phase CT Images.
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DOI：
10.1109/embc48229.2022.9871539
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发表时间：
2022-07-01
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影响因子：
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通讯作者：
Chen, Yen-Wei
中科院分区：
文献类型：
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作者：
Jain, Rahul Kumar;Sato, Takahiro;Chen, Yen-Wei
在肝脏肿瘤的计算机辅助诊断中，多期CT图像的自动、高效检测是必不可少的。如今，深度学习已经广泛应用于医疗领域。通常情况下，基于深度学习的人工智能系统需要大量的训练数据，但在医疗领域，获取足够的训练数据和高质量的注释是一个重大挑战。为了解决训练数据缺乏的问题，基于领域自适应的方法是近年来发展起来的一种跨越具有不同特征特征和数据分布的数据集的领域差距的技术。提出了一种基于区域自适应的多期CT图像肝脏肿瘤检测方法。我们采用了从PV相图像到ART和NC相图像的模型学习知识。临床相关性-为了最大限度地减少领域差距，我们采用了一种对抗性学习方案，使用无锚点检测器对中级输出特征图进行最大的平方损失。实验表明，该方法对不同ct相位图像的训练效果优于常规训练。
Automatic and efficient liver tumor detection in multi-phase CT images is essential in computer-aided diagnosis of liver tumors. Nowadays, deep learning has been widely used in medical applications. Normally, deep learning-based AI systems need a large quantity of training data, but in the medical field, acquiring sufficient training data with high-quality annotations is a significant challenge. To solve the lack of training data issue, domain adaptation-based methods have recently been developed as a technique to bridge the domain gap across datasets with different feature characteristics and data distributions. This paper presents a domain adaptation-based method for detecting liver tumors in multi-phase CT images. We adopt knowledge for model learning from PV phase images to ART and NC phase images. Clinical Relevance- To minimize the domain gap we employ an adversarial learning scheme with the maximum square loss for mid-level output feature maps using an anchorless detector. Experiments show that our proposed method performs much better for various CT-phase images than normal training.