EnTrust: Modeling Inter-Modal Conflict for Trustworthy Multimodal Medical Image Analysis

• URL: http://arxiv.org/abs/2606.21384v1
• Date: Fri, 19 Jun 2026 12:43:50 GMT
• Title: EnTrust: Modeling Inter-Modal Conflict for Trustworthy Multimodal Medical Image Analysis
• Abstract summary: 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.
• Score: 41.67522319355107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal medical imaging fuses complementary anatomical and functional information, yet modalities frequently disagree in pathologically heterogeneous regions. Current segmentation models handle this in one of two inadequate ways: deterministic fusion that averages away disagreement, or post-hoc uncertainty estimation decoupled from the fusion process that produces it. Both obscure the clinically critical question: why is this prediction unreliable? We present EnTrust, a framework that treats inter-modal conflict as the primary source of predictive uncertainty. Our EnFuse module decomposes multimodal features into three disentangled components: shared anatomical consensus (F_c), modality-specific cues (F_{u,m}), and spatially localized conflict signals (F_{cf}), with independence enforced via a cross-covariance objective. This structured decomposition conditions SegDiff, a diffusion-based generative segmentation model whose sampled hypotheses diverge specifically in regions of modal disagreement. TrustMap then translates this hypothesis divergence into calibrated, pixel-wise uncertainty using ensemble entropy, conflict-guided perturbation probing, and a learned calibration head, enabling clinicians to understand not only where predictions are uncertain, but why. Across four benchmarks spanning brain, cardiac, lesion, and oncology domains, EnTrust achieves state-of-the-art segmentation accuracy while reducing calibration error by 40% compared to the strongest baseline. Notably, it outperforms 5x deep ensembles using a single model at roughly half the memory footprint. Code and checkpoints are available at https://github.com/GenMI-Lab/EnTrust.git.

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