Enhancing Liver Fibrosis Measurement: Deep Learning and Uncertainty Analysis Across Multi-Centre Cohorts

Digital pathology enables large multi-centre studies of histological specimens, but differences in staining protocols and slide quality can compromise the comparability of quantitative results. We analysed 686 PSR-stained liver biopsies from four independent cohorts spanning more than 20 clinical sites to assess how stain variability affects automated fibrosis quantification and model uncertainty. A U-Net ensemble was trained to segment collagen and to estimate pixel- and tile-level predictive uncertainty. Across markedly heterogeneous staining conditions, the ensemble achieved strong segmentation performance (Dice 0.83–0.90) and produced informative uncertainty maps that identified artefacts and out-of-distribution regions. Epistemic uncertainty values were typically below 0.002, providing a practical criterion for flagging unreliable predictions. Our results demonstrate that ensemble-based uncertainty estimation complements stain-standardisation efforts by quantifying prediction confidence directly from model outputs, improving the reliability and interpretability of collagen proportionate-area measurements across multi-centre datasets. This framework supports more trustworthy and reproducible digital-pathology workflows for fibrosis assessment and other histological applications. A retrospective cohort of liver biopsies collected from over 20 healthcare centres has been assembled. The cohort is characterized on the basis of collagen staining used for liver fibrosis assessment. A computational pipeline for the quantification of collagen from liver histology slides has been developed and applied to the described cohorts. Uncertainty estimation is evaluated as a method to build trust in deep-learning based collagen predictions.