让不同说法都给出一致的细胞分割结果，提升医学图像分割可靠性
Prompt Group-Aware Training for Robust Text-Guided Nuclei Segmentation
- 将语义相似的提示分组，通过一致性约束训练提升鲁棒性
- 在六个跨数据集任务中平均提升Dice 2.16点，性能波动显著降低
- 无需修改模型结构，适合临床病理场景的文本引导分割应用
像SAM3这样的基础模型虽支持灵活的文本引导医学图像分割，但其预测对提示格式极为敏感。即使语义等价的描述也会产生不一致的分割掩码，影响临床与病理科流程的可靠性。本文将提示敏感性重构为分组一致性问题：将语义相关的提示归入同一提示组，共享相同真值掩码，并提出一种提示组感知训练框架，用于鲁棒的文本引导细胞分割。该方法结合（i）基于分割损失的隐式排序信号的质量引导组正则化，以及（ii）采用停止梯度策略的逐逻辑层一致性约束，以对齐组内预测。该方法无需架构改动，推理过程保持不变。在多个细胞分割基准数据集上的大量实验表明，该方法在文本提示下表现持续提升，且在不同提示质量水平下性能方差显著减小。在六个零样本跨数据集任务中，平均Dice提升2.16点。结果表明，该方法显著增强了视觉-语言分割在计算病理学中的鲁棒性与泛化能力。
原文摘要 · Abstract (English)
Foundation models such as Segment Anything Model 3 (SAM3) enable flexible text-guided medical image segmentation, yet their predictions remain highly sensitive to prompt formulation. Even semantically equivalent descriptions can yield inconsistent masks, limiting reliability in clinical and pathology workflows. We reformulate prompt sensitivity as a group-wise consistency problem. Semantically related prompts are organized into \emph{prompt groups} sharing the same ground-truth mask, and a prompt group-aware training framework is introduced for robust text-guided nuclei segmentation. The approach combines (i) a quality-guided group regularization that leverages segmentation loss as an implicit ranking signal, and (ii) a logit-level consistency constraint with a stop-gradient strategy to align predictions within each group. The method requires no architectural modification and leaves inference unchanged. Extensive experiments on multi-dataset nuclei benchmarks show consistent gains under textual prompting and markedly reduced performance variance across prompt quality levels. On six zero-shot cross-dataset tasks, our method improves Dice by an average of 2.16 points. These results demonstrate improved robustness and generalization for vision-language segmentation in computational pathology.
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