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arXiv · 2605.12562

Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

Bo Peng·Wujian Xu·Kun Wang·Ximing Liao·Na Wang·Daqian Shi·Tian Li·Jing Gao·Johan Thygesen·Yingqun Ji·Honghan Wu
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Abstract

Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learning methods fuse representations only at later stages, missing cross-density interactions. We propose a cross-window knowledge distillation framework in which student encoders learn latent clinical priors from a teacher trained on the most informative window. Evaluated retrospectively on three cohorts - COPD-CT-DF (n=719), RSNA PE (n=1,433), and an in-house CTEPD dataset (n=161) - distillation improved per-window AUC by 10.1-16.5 percentage points on COPD-CT-DF (0.75-0.81 to 0.90-0.94; all P<0.001), with ensemble AUC reaching 0.9960. Similar gains were observed on RSNA PE (0.80-0.83 to 0.90-0.92) and CTEPD (AUC 0.7481 vs. 0.6264). Cross-window distillation internalises pathological signatures invisible to supervised approaches, offering a generalisable solution for multi-window pulmonary CT analysis.

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Bo Peng, Wujian Xu, Kun Wang, Ximing Liao, Na Wang, Daqian Shi, Tian Li, Jing Gao, Johan Thygesen, Yingqun Ji, Honghan Wu. 2026-05-12. Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation. https://arxiv.org/abs/2605.12562

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