通过动态焦点注意机制学习不均衡病理分割中的类别难度
Learning Class Difficulty via Dynamic Focal Attention for Histopathology Segmentation
AI总结：
本文提出动态焦点注意机制，通过查询基于掩码解码器的交叉注意机制直接学习类别难度，提升不均衡数据下的分割性能，实验表明其在三个病理基准测试中优于传统方法。
摘要：
在病理图像语义分割中，类别不平衡通常通过频率为基础的损失重加权解决，但真正困难来源于形态变化、边界模糊和上下文相似性等因素。本文提出动态焦点注意机制（DFA），通过查询基于掩码解码器的交叉注意机制直接学习类别特定难度。DFA引入可学习的类别偏置到注意logits，实现表示层面的重加权而非梯度层面的重加权。初始化从对数频率先验防止梯度饥饿，偏置通过端到端优化，使模型通过训练自适应捕捉难度信号，有效统一频率基于和难度意识方法。在三个病理基准测试（BDSA、BCSS、CRAG）中，DFA一致提高Dice和IoU，匹配或超过难度意识基线，无需额外估计器或训练阶段。这些结果表明，在表示层面编码类别难度为不均衡分割提供了一种原理性的替代方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
