Jain Rahul Kumar, Sato Takahiro, El-Sayed Ahmed M, Watasue Taro, Nakagawa Tomohiro, Iwamoto Yutaro, Li Yinhao, Han Xianhua, Lin Lanfen, Hu Hongjie, Ruan Xiang, Chen Yen-Wei
Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10340608.
Computer-aided diagnostic methods, such as automatic and precise liver tumor detection, have a significant impact on healthcare. In recent years, deep learning-based liver tumor detection methods in multi-phase computed tomography (CT) images have achieved noticeable performance. Deep learning frameworks require a substantial amount of annotated training data but obtaining enough training data with high quality annotations is a major issue in medical imaging. Additionally, deep learning frameworks experience domain shift problems when they are trained using one dataset (source domain) and applied to new test data (target domain). To address the lack of training data and domain shift issues in multiphase CT images, here, we present an adversarial learning-based strategy to mitigate the domain gap across different phases of multiphase CT scans. We introduce to use Fourier phase component of CT images in order to improve the semantic information and more reliably identify the tumor tissues. Our approach eliminates the requirement for distinct annotations for each phase of CT scans. The experiment results show that our proposed method performs noticeably better than conventional training and other methods.
计算机辅助诊断方法，如自动精确的肝脏肿瘤检测，对医疗保健有着重大影响。近年来，基于深度学习的多期计算机断层扫描（CT）图像肝脏肿瘤检测方法取得了显著成效。深度学习框架需要大量带注释的训练数据，但获取足够的高质量注释训练数据是医学成像中的一个主要问题。此外，深度学习框架在使用一个数据集（源域）进行训练并应用于新的测试数据（目标域）时会遇到域转移问题。为了解决多期CT图像中训练数据不足和域转移问题，在此，我们提出一种基于对抗学习的策略来减轻多期CT扫描不同阶段之间的域差距。我们引入使用CT图像的傅里叶相位分量，以改善语义信息并更可靠地识别肿瘤组织。我们的方法消除了对CT扫描每个阶段进行单独注释的要求。实验结果表明，我们提出的方法比传统训练和其他方法表现明显更好。
