{"ID":2841838,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11486","arxiv_id":"2511.11486","title":"Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H\u0026E Images","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\u0026E-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\u0026E-based PD-L1 prediction is a promising step toward scalable, interpretable biomarker assessment in clinical workflows.","short_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\u0026E-stained histology images using Multimodal Posterior Sampling (...","url_abs":"https://arxiv.org/abs/2511.11486","url_pdf":"https://arxiv.org/pdf/2511.11486v1","authors":"[\"Roman Kinakh\",\"Gonzalo R. Ríos-Muñoz\",\"Arrate Muñoz-Barrutia\"]","published":"2025-11-14T17:05:13Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"q-bio.QM\"]","methods":"[]","has_code":false}