Abstract
Conformal risk control (CRC) provides distribution-free segmentation guarantees by calibrating a prediction-set threshold on held-out data. In federated deployments, the standard approach pools calibration scores into a single threshold. We quantify, on real multi-institutional brain tumor data (FeTS-2022, 1,251 subjects, 20 institutions), a critical failure: naive pooled CRC protects the average hospital but violates coverage at 40% of individual institutions, with the worst site exceeding the target false-negative rate by 7.8 percentage points. We trace this failure to a hidden design choice: the aggregation weights implicitly determine whose coverage is protected. Sample-size weighting optimizes patient-level validity but can sacrifice institution-level reliability; equal-site weighting improves institution-level reliability on this benchmark at comparable efficiency, using only a single scalar per site. We propose risk-curve shrinkage as a principled mechanism: each site transmits its empirical risk curve (G scalars) and a single hyperparameter n_0 smoothly interpolates between site-specific local calibration and sample-size-weighted pooled calibration. Leave-one-site-out sensitivity analysis identifies n_0=19, achieving 2.7/20 violations at 2.0× stretch. Direct Lagrangian budget optimization fails by concentrating risk on vulnerable hospitals; the finite-sample correction term is essential: removing it triples violations. No patient-level images, masks, or per-volume scores leave any site.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/DeCaF_004.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=pe2Lr0Zdlq
BibTex
@InProceedings{ShaNaf_When_MICCAISAT2026,
author = { Shahid, Nafis Fuad},
title = { { When Average Calibration Fails: Site-Conditional Federated Conformal Risk Control } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17252},
month = {pending},
page = {pending}
}