Title: MR–HCP: Morphology-Regularized Hierarchical Conformal Prediction for TEM of Subcellular Ultrastructure
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Conformal Prediction; Transmission Electron Microscopy; Uncertainty Quantification

Abstract: Reliable uncertainty quantification is critical for deploying deep learning models in biomedical imaging, where fine-grained structures often exhibit overlapping morphology and ambiguous boundaries. We introduce Morphology-Regularized Hierarchical Conformal Prediction (MR-HCP), a novel framework that combines hierarchical taxonomies with morphology-aware nonconformity to provide compact prediction sets with rigorous coverage guarantees. Unlike existing conformal methods that operate on flat label spaces and probability-only scores, MR-HCP leverages a two-stage super to fine calibration and penalizes morphological deviations from class prototypes. We integrate MR-HCP with a YOLOv11 detector to demonstrate an end-to-end pipeline for single-cell TEM analysis. On a curated neutrophil ultrastructure dataset (around 4.9k annotations), MR-HCP achieves near-nominal coverage (0.954), small average set size (1.205), and high singleton accuracy (0.934), significantly outperforming Split CP, Mondrian CP, and APS baselines. Beyond inference, MR-HCP facilitates semi-automatic annotation and dataset reclassification, systematically refining coarse expert labels into fine categories while transparently quantifying uncertainty. Our results establish MR-HCP as a generalizable approach for uncertainty-aware classification in domains with hierarchical and morphology-rich categories, with applications across biomedical and scientific imaging.

TL;DR: MR–HCP is a novel morphology-regularized hierarchical conformal method that delivers calibrated uncertainty (reliable, small prediction sets) for subcellular ultrastructure in TEM images