Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H&E Images

• URL: http://arxiv.org/abs/2511.11486v1
• Date: Fri, 14 Nov 2025 17:05:13 GMT
• Title: Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H&E Images
• Authors: Roman Kinakh, Gonzalo R. Ríos-Muñoz, Arrate Muñoz-Barrutia,
• Abstract summary: We present nnUNet-B: a Bayesian segmentation framework that infers PD-L1 expression directly from H&E-stained histology images.<n> Evaluated on a dataset of lung squamous cell carcinoma, our approach achieves competitive performance against established baselines.<n>These results suggest that uncertainty-aware H&E-based PD-L1 prediction is a promising step toward scalable, interpretable biomarker assessment.
• Score: 0.4697971908036153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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