CARD: Calibration via Agreement in Reverse Diffusion for Out-of-Domain MRI Segmentation

• URL: http://arxiv.org/abs/2608.28681v1
• Date: Wed, 26 Aug 2026 07:23:08 GMT
• Title: CARD: Calibration via Agreement in Reverse Diffusion for Out-of-Domain MRI Segmentation
• Abstract summary: We propose CARD, which maps the temporal aggregate of disagreement to a temperature field applied per pixel across all classes, so that confidence changes while the segmentation does not.<n>Across cardiac, prostate and brain MRI shifts, CARD lowers calibration error in 45 of 49 comparisons against the strongest baseline in each setting.
• Score: 14.844162865265673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Probability calibration aligns model confidence with predictive accuracy, enabling clinicians to identify unreliable segmentation regions. This alignment breaks down under domain shift, where artifacts and unseen protocols produce confident errors. Existing post-hoc methods adapt the correction at test time, conditioning on predictive entropy, the logit pattern, or augmentation response, but each proxy is read from the terminal prediction, the very quantity that shift corrupts. This motivates reliability evidence beyond the terminal prediction, which categorical diffusion provides in two ways. First, a generative shape prior keeps a capacity-limited reference intact when appearance is corrupted, so its disagreement with the primary segmentor highlights primary-model errors. Second, every reverse step yields a class distribution, separating persistent disagreement from transient discrepancy. Aggregated over the trajectory, this disagreement correlates with Dice at 0.788, against 0.521 for a matched discriminative control. We therefore propose CARD (Calibration via Agreement in Reverse Diffusion), which maps the temporal aggregate of this disagreement to a temperature field applied per pixel across all classes, so that confidence changes while the segmentation does not. Across cardiac, prostate and brain MRI shifts, CARD lowers calibration error in 45 of 49 comparisons against the strongest baseline in each setting.

Related papers

• Symmetry-Aware Likelihood-Orbit Aggregation for Selective Left-Right Claim Verification [14.108900142001628]
  We introduce Relation-Orbit, a closed-form contrast that assigns eight normalized likelihoods to query-supporting and counterfactual roles.<n>On VSR and GQA across four frozen vision-language models, Relation-Orbit yields higher mean test coverage at a 10% selective-risk calibration target.
  arXiv  Detail & Related papers  (2026-09-15T11:19:03Z)
• Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations [42.90321348046055]
  We propose an annotation-free attribution regularization framework based on submodular search over image regions.<n>By measuring how candidate subsets affect model outputs, the search extracts compact, class-discriminative evidence as search-derived supervision.<n> Experiments on ImageNet-100 show that our method substantially improves attribution stability, Insertion, and Deletion on ViT-B/16 with only a 0.28-point accuracy drop.
  arXiv  Detail & Related papers  (2026-07-26T20:41:38Z)
• Anytime-Valid Confirmation of Covariate Balance for Prespecified Corrections [0.6599344783327052]
  We study anytime-valid confirmation of prespecified corrections from sequential unlabeled target inputs.<n>Experiments illustrate false-confirmation control, locality to the balancing-function class, KL-drift diagnostics, acceptable-region monitoring, finite-source effects.
  arXiv  Detail & Related papers  (2026-07-25T11:21:17Z)
• Diagnosing Correctness Probes under Self-Judgement Confounding [1.2691047660244335]
  Hidden-state readouts can predict whether language-model outputs are correct, but objective correctness (OC) usually agrees with the model's own self-judgement ( SJ)<n>We construct conflict cases in which OC and SJ predict opposite readout orderings.<n>We estimate factorial SJ- and OC-associated directions and evaluate their polarity across mathematical reasoning and factual recall.
  arXiv  Detail & Related papers  (2026-07-18T12:45:49Z)
• EnTrust: Modeling Inter-Modal Conflict for Trustworthy Multimodal Medical Image Analysis [41.67522319355107]
  EnTrust is a framework that treats inter-modal conflict as the primary source of predictive uncertainty.<n>It achieves state-of-the-art segmentation accuracy while reducing calibration error by 40% compared to the strongest baseline.
  arXiv  Detail & Related papers  (2026-06-19T12:43:50Z)
• Anytime-Valid Confirmation of Label-Shift Corrections [0.6599344783327052]
  In small-batch scientific deployments, labeled target outcomes may be too scarce for reliable shift estimation.<n>We show that the per-observation likelihood ratio between a label-shift-corrected predictive and the source predictive is a conditional e-value.
  arXiv  Detail & Related papers  (2026-06-12T02:03:21Z)
• Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection [56.362776482614976]
  Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection.<n>We show that this protocol can become ill-posed when the held-out anomaly class overlaps the normal mixture in representation space.<n>We introduce a simple training-free diagnostic, neighborhood class leakage, and show that it predicts score-direction instability across Fashion-MNIST, CIFAR-10, and Imagenette.
  arXiv  Detail & Related papers  (2026-05-23T06:27:42Z)
• Perturb and Correct: Post-Hoc Ensembles using Affine Redundancy [0.0]
  We introduce Perturb-and-Correct (P&C), a method for constructing diverse predictors from a single pretrained network.<n>P&C applies random hidden layer perturbations with a least-squares correction in the subsequent affine layer, producing predictors that agree on calibration data while remaining free to disagree away from it.<n> Empirically, P&C achieves a strong ID/OOD tradeoff across MuJoCo dynamics prediction and CIFAR-10 OOD detection, matching or outperforming standard post-hoc baselines while requiring only a single pretrained model.
  arXiv  Detail & Related papers  (2026-05-02T22:48:11Z)
• Correction and Corruption: A Two-Rate View of Error Flow in LLM Protocols [51.56484100374058]
  We propose a paired-outcome measurement interface for auditing a single protocol step on exact-match tasks.<n>For each instance, the interface records a baseline correctness bit and a post-step correctness bit.<n>These rates predict accuracy changes and define a reusable empirical interface testable across seeds, mixtures, and pipelines.
  arXiv  Detail & Related papers  (2026-04-20T13:25:40Z)
• LATA: Laplacian-Assisted Transductive Adaptation for Conformal Uncertainty in Medical VLMs [61.06744611795341]
  Medical vision-language models (VLMs) are strong zero-shot recognizers for medical imaging.<n>We propose texttttextbfLATA (Laplacian-Assisted Transductive Adaptation), a textittraining- and label-free refinement.<n>texttttextbfLATA sharpens zero-shot predictions without compromising exchangeability.
  arXiv  Detail & Related papers  (2026-02-19T16:45:38Z)
• BURN: Backdoor Unlearning via Adversarial Boundary Analysis [73.14147934175604]
  Backdoor unlearning aims to remove backdoor-related information while preserving the model's original functionality.<n>We propose Backdoor Unlearning via adversaRial bouNdary analysis (BURN), a novel defense framework that integrates false correlation decoupling, progressive data refinement, and model purification.
  arXiv  Detail & Related papers  (2025-07-14T17:13:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.