Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization

• URL: http://arxiv.org/abs/2507.19858v1
• Date: Sat, 26 Jul 2025 08:23:43 GMT
• Title: Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization
• Authors: Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, Chih-Chung Hsu,
• Abstract summary: SSFL++ and KDS pipeline perform spatial and temporal standardization to reduce inter-source variance.<n>This study analyzes how this input-space standardization manages the trade-off between local discriminability and cross-source generalization.
• Score: 5.501560446935927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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