通过输入空间标准化提升多源CT图像分类的跨机构泛化能力。
Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization
- 采用空间-时序标准化预处理，统一不同来源的图像分布。
- 在多个模型上实现稳定性能提升，验证预处理有效性。
- 适合医疗影像跨机构应用，尤其在数据差异大的场景。
多源CT图像分类受域偏移影响，导致跨机构泛化能力下降。尽管结合空间切片特征学习（SSFL++）与基于核密度的切片采样（KDS）的预处理流程已展现实际效果，但其域鲁棒性的机制仍不明确。本研究分析该输入空间标准化方法如何平衡局部判别性与跨源泛化性。SSFL++与KDS流程通过空间与时间标准化，降低源间差异，将异构输入映射至一致目标空间，实现前置对齐，缓解域偏移并简化网络优化任务。实验验证表明，该方法在多种架构上均带来一致性能提升，证明优势源于预处理本身。该方案在竞赛中获得第一名，支持输入空间标准化是多机构医学影像中一种稳健且实用的解决方案。
原文摘要 · Abstract (English)
Multi-source CT-scan classification suffers from domain shifts that impair cross-source generalization. While preprocessing pipelines combining Spatial-Slice Feature Learning (SSFL++) and Kernel-Density-based Slice Sampling (KDS) have shown empirical success, the mechanisms underlying their domain robustness remain underexplored. This study analyzes how this input-space standardization manages the trade-off between local discriminability and cross-source generalization. The SSFL++ and KDS pipeline performs spatial and temporal standardization to reduce inter-source variance, effectively mapping disparate inputs into a consistent target space. This preemptive alignment mitigates domain shift and simplifies the learning task for network optimization. Experimental validation demonstrates consistent improvements across architectures, proving the benefits stem from the preprocessing itself. The approach's effectiveness was validated by securing first place in a competitive challenge, supporting input-space standardization as a robust and practical solution for multi-institutional medical imaging.
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