用H&E图像预测PD-L1表达，同时给出置信度图。
Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H&E Images
- 通过多模态后验采样从多个训练节点中提取不确定性。
- 在肺鳞癌数据集上达到0.805的平均Dice和0.709的IoU。
- 生成像素级不确定性图，帮助医生判断结果可靠性。
准确评估PD-L1表达对指导免疫治疗至关重要，但现有基于免疫组化（IHC）的方法成本高。本文提出nnUNet-B：一种基于贝叶斯分割框架，通过多模态后验采样（MPS）直接从H&E染色组织切片中推断PD-L1表达。该方法在nnUNet-v2基础上，利用循环训练中采样的多个模型检查点近似后验分布，实现精准分割与认知不确定性估计（基于熵和标准差）。在肺鳞状细胞癌数据集上，其平均Dice分数和平均交并比分别达0.805和0.709，性能媲美现有基准，并生成像素级不确定性图。不确定性估计与分割误差呈强相关性，但校准仍有不足。结果表明，具备不确定性感知能力的H&E基PD-L1预测是迈向可扩展、可解释的临床生物标志物评估的重要一步。
原文摘要 · Abstract (English)
Accurate assessment of PD-L1 expression is critical for guiding immunotherapy, yet current immunohistochemistry (IHC) based methods are resource-intensive. We present nnUNet-B: a Bayesian segmentation framework that infers PD-L1 expression directly from H&E-stained histology images using Multimodal Posterior Sampling (MPS). Built upon nnUNet-v2, our method samples diverse model checkpoints during cyclic training to approximate the posterior, enabling both accurate segmentation and epistemic uncertainty estimation via entropy and standard deviation. Evaluated on a dataset of lung squamous cell carcinoma, our approach achieves competitive performance against established baselines with mean Dice Score and mean IoU of 0.805 and 0.709, respectively, while providing pixel-wise uncertainty maps. Uncertainty estimates show strong correlation with segmentation error, though calibration remains imperfect. These results suggest that uncertainty-aware H&E-based PD-L1 prediction is a promising step toward scalable, interpretable biomarker assessment in clinical workflows.
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