Learning Class Difficulty via Dynamic Focal Attention for Histopathology Segmentation

Abstract

Frequency-based loss reweighting, the standard remedy for imbalanced histopathology segmentation, implicitly assumes that rare classes are difficult. Yet difficulty also arises from morphological variability, boundary ambiguity, and contextual similarity, all largely orthogonal to class frequency. We propose Dynamic Focal Attention (DFA), a simple, efficient mechanism that learns class-specific difficulty directly within the cross-attention of query-based mask decoders. DFA adds a learnable per-class bias to the attention logits, reweighting representations before prediction rather than gradients after it. Initialised from a centred log-frequency prior to prevent gradient starvation and then optimised end-to-end, the bias adapts to difficulty signals as they emerge during training, unifying frequency- and difficulty-aware reweighting in a single attention-bias framework. On three benchmarks (BCSS, BDSA, CRAG), DFA consistently improves Dice and IoU, matching or exceeding a two-stage difficulty-aware baseline without a separate estimator or extra training stage. This shows that encoding difficulty at the representation level is a principled alternative to conventional loss reweighting.