用循环GAN无监督适配不同MRI扫描仪图像，提升模型泛化能力。
Fine-Tuning Cycle-GAN for Domain Adaptation of MRI Images
- 基于循环GAN构建双向映射，无需配对数据即可完成图像域适配。
- 在多个MRI数据集上验证，显著降低域间差异，提升模型性能。
- 适用于医疗影像跨设备迁移，助力精准诊断与模型一致性提升。
不同扫描仪或机构获取的磁共振成像（MRI）常因硬件、协议和采集参数差异产生域偏移，导致在源域训练的深度学习模型在目标域上性能下降。本文提出一种基于循环GAN的无监督医学图像域适应方法，利用循环GAN学习源域与目标域间的双向映射，无需配对训练数据，同时保持图像解剖结构完整性。通过引入内容损失与差异损失，确保域适配过程中的图像保真度。在多个MRI数据集上的实验表明，该方法可实现双向域适应且无需标签数据，统计结果证实其有效降低域相关变异性，提升模型性能，为提高医疗诊断准确性提供了新路径。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI) scans acquired from different scanners or institutions often suffer from domain shifts owing to variations in hardware, protocols, and acquisition parameters. This discrepancy degrades the performance of deep learning models trained on source domain data when applied to target domain images. In this study, we propose a Cycle-GAN-based model for unsupervised medical-image domain adaptation. Leveraging CycleGANs, our model learns bidirectional mappings between the source and target domains without paired training data, preserving the anatomical content of the images. By leveraging Cycle-GAN capabilities with content and disparity loss for adaptation tasks, we ensured image-domain adaptation while maintaining image integrity. Several experiments on MRI datasets demonstrated the efficacy of our model in bidirectional domain adaptation without labelled data. Furthermore, research offers promising avenues for improving the diagnostic accuracy of healthcare. The statistical results confirm that our approach improves model performance and reduces domain-related variability, thus contributing to more precise and consistent medical image analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。