EnTrust: Modeling Inter-Modal Conflict for Trustworthy Multimodal Medical Image Analysis
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), modalit...
| Authors | Dwarikanath Mahapatra, Abhijit Das, Behzad Bozorgtabar et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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