TriPoD: Triadic 3D CT Representation Learning via Physically Anchored Pretraining and Polarity-Debiased Text Alignment

General-purpose foundation models for 3D computed tomography (CT) should transfer across global recognition, dense spatial prediction, and language-guided inference. Yet standardized resampling obscures scan-specific acquisition geometry, while report-derived supervision can admit textual shortcuts when concepts are associated with a dominant polarity. We present **TriPod**, a triadic framework combining physically anchored volumetric pretraining with polarity-debiased text alignment. Pretrained on more than 470,000 quality-controlled CT volumes from multiple medical centers, TriPod preserves native voxel lattices and voxel-to-world transforms instead of resampling entire scans isotropically. Affine-aware 3D rotary positional encoding maps patch centers into view-centered physical space, representing scale and orientation across acquisitions. For locked-image CT–report alignment, a patient-disjoint text-only audit reveals phrase-conditioned polarity leakage. Polarity-Debiased Opposite Sentence Learning (PD-OSL) addresses this shortcut by combining evidence-constrained atomic findings with polarity-debiased sampling. We evaluate the shared visual encoder across classification, segmentation, and internal and external zero-shot recognition. It transfers strongly across all three regimes. Controlled ablations show small, task-dependent effects of physical coordinates on global recognition and dataset-dependent effects on dense transfer, with a clear gain on one large multi-organ benchmark but negligible change on another. Under matched checkpoint and prompt conditions, PD-OSL reduces phrase-conditioned leakage and improves zero-shot recognition on both cohorts. These results highlight acquisition-aware geometry and shortcut-resistant language supervision as complementary design principles for transferable 3D CT representation learning.