Reassessing Spatial Context in Histopathology

Histopathology classification often depends on both local morphology and surrounding tissue architecture, yet effectively incorporating spatial context into patch-level prediction remains challenging. We study contextual patch classification using a shared transformer that jointly processes and supervises a center patch and its $5\times 5$ spatial neighborhood, encouraging representations that preserve local evidence while exploiting broader tissue context. We further investigate a cross-slide contextual-update regularizer designed to constrain how neighborhood information modifies center predictions. Across 64,921 test patches, two frozen pathology foundation-model encoders, and three seeds, our dual-view training recipe improves slide-bootstrap ensemble macro-F1 over a strong contextual baseline by 2.03 percentage points with UNI2-h (95% CI $[1.10,3.13]$, $p<0.001$) and 0.86 points with ALICE (95% CI $[-0.08,2.00]$, $p=0.072$). The additional cross-slide regularizer provides no further improvement. These results demonstrate that jointly learning from local and spatially contextualized views provides a simple and effective approach to contextual histopathology classification, with consistent gains across foundation-model representations.