RLT: Learning from Relational Long Tails in Whole Slide Image Analysis

Long-tailed whole slide image (WSI) classification is usually studied from the imbalance of slide labels or sparse instance-level evidence. We argue that this view is incomplete. Diagnostic evidence in pathology is also encoded by interactions among heterogeneous tissues, while these tissue relations are themselves highly imbalanced. Common relations dominate graph propagation, whereas rare but class-discriminative relations can be suppressed, especially for tail classes with few training slides. We therefore propose RLT, a relational long-tail framework for WSI classification. First, we construct a morphology-aware heterogeneous graph in which patch nodes are associated with tissue semantics and relational edges encode tissue interactions. Second, we introduce diagnosticity-aware relation debiasing, which combines relation rarity with counterfactual contribution to enhance rare relations only when they provide class-specific evidence. Third, we develop prototype-guided representation enrichment to densify the sparsely sampled representation regions of data-scarce classes and stabilize the decision space. A class-prior-aware Balanced Softmax objective is further used to reduce residual classifier bias. Experiments on Camelyon+-LT and PANDA-LT demonstrate that RLT consistently outperforms representative baselines in both overall and tail-class performance. Code and configurations are available at: https://anonymous.4open.science/r/RLT_ICLR2027-anonymous/.