解决多中心脑部MRI图像域差异，提升病灶检索准确率
Domain-invariant feature learning in brain MR imaging for content-based image retrieval
- 用风格编码器对抗性域适应分离设备差异信息
- 在8个公开数据集上疾病检索准确率最高
- 适合多中心医学影像分析与跨机构图像检索
大规模脑部MRI研究常因不同医疗机构的设备和扫描协议差异产生域偏移，成为近年关键挑战。本文提出一种低维表示获取方法——风格编码器对抗性域适应（SE-ADA），实现脑部MRI内容驱动图像检索（CBIR）。SE-ADA通过分离低维表示中的域特异性信息，并利用对抗学习最小化域间差异，同时保留病理特征。在包含ADNI1/2/3、OASIS1/2/3/4、PPMI共8个公开脑部MRI数据集上的对比实验表明，SE-ADA有效消除域信息，保持原始脑结构关键特征，并实现了最高的疾病搜索准确率。
原文摘要 · Abstract (English)
When conducting large-scale studies that collect brain MR images from multiple facilities, the impact of differences in imaging equipment and protocols at each site cannot be ignored, and this domain gap has become a significant issue in recent years. In this study, we propose a new low-dimensional representation (LDR) acquisition method called style encoder adversarial domain adaptation (SE-ADA) to realize content-based image retrieval (CBIR) of brain MR images. SE-ADA reduces domain differences while preserving pathological features by separating domain-specific information from LDR and minimizing domain differences using adversarial learning. In evaluation experiments comparing SE-ADA with recent domain harmonization methods on eight public brain MR datasets (ADNI1/2/3, OASIS1/2/3/4, PPMI), SE-ADA effectively removed domain information while preserving key aspects of the original brain structure and demonstrated the highest disease search accuracy.
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