Learning Class Difficulty in Imbalanced Histopathology Segmentation via Dynamic Focal Attention

• URL: http://arxiv.org/abs/2604.13479v1
• Date: Wed, 15 Apr 2026 05:06:39 GMT
• Title: Learning Class Difficulty in Imbalanced Histopathology Segmentation via Dynamic Focal Attention
• Abstract summary: Dynamic Focal Attention (DFA) learns class-specific difficulty directly within the cross-attention of query-based mask decoders.<n>DFA consistently improves Dice and IoU on three histopathology benchmarks.
• Score: 0.34785805439031336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic segmentation of histopathology images under class imbalance is typically addressed through frequency-based loss reweighting, which implicitly assumes that rare classes are difficult. However, true difficulty also arises from morphological variability, boundary ambiguity, and contextual similarity-factors that frequency cannot capture. We propose Dynamic Focal Attention (DFA), a simple and efficient mechanism that learns class-specific difficulty directly within the cross-attention of query-based mask decoders. DFA introduces a learnable per-class bias to attention logits, enabling representation-level reweighting prior to prediction rather than gradient-level reweighting after prediction. Initialised from a log-frequency prior to prevent gradient starvation, the bias is optimised end-to-end, allowing the model to adaptively capture difficulty signals through training, effectively unifying frequency-based and difficulty-aware approaches under a common attention-bias framework. On three histopathology benchmarks (BDSA, BCSS, CRAG), DFA consistently improves Dice and IoU, matching or exceeding a difficulty-aware baseline without a separate estimator or additional training stage. These results demonstrate that encoding class difficulty at the representation level provides a principled alternative to conventional loss reweighting for imbalanced segmentation.

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