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

ArXi:2605.12562v1 Announce Type: cross 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.