PPFuse: Fuse What Aligns, Preserve What Does Not

Frozen pathology foundation models provide complementary representations of the same tissue patch, motivating feature fusion for whole-slide image classification. Aligning a lower-dimensional auxiliary representation to an anchor subspace leaves the treatment of the anchor orthogonal complement unspecified. Full-space interpolation implicitly ties complement scaling to the reachable-space mixing weight, although the correspondence does not require this coupling. We introduce PPFuse, which fits a semi-orthogonal correspondence from paired training features without task labels and decouples these decisions. After scale-matched mixing in the reachable coordinates, PPFuse adopts a minimum-change principle relative to the anchor: for the fixed correspondence and fused coordinates, the unique closest reconstruction in Frobenius distance preserves the complement at its native scale. The resulting transform is closed-form and retains the anchor dimensionality. Across three multiclass whole-slide datasets and three multiple instance learning backbones, PPFuse achieves higher mean balanced accuracy than both individual encoders in eight of nine settings and ranks first among the evaluated methods in seven. Across five few-shot sampling seeds, PPFuse exceeds both individual encoders in mean ROC-AUC across all nine backbone-shot settings. Component analyses show that the retained complement carries predictive signal and support native-scale preservation as a competitive fixed default across the evaluated datasets and backbones. Alignment controls further show that downstream adaptation narrows the performance gap between fitted and random alignments, highlighting the distinction between geometric alignment and predictive utility. Additional natural-image and image-text experiments demonstrate competitive performance against the evaluated fusion baselines while retaining the anchor dimensionality.