Missing or degraded sequences can limit prostate multiparametric MRI. We developed MSCNet, a sequence-conditioned cross-modal generative framework for reconstructing unavailable contrasts and restoring degraded acquisitions. Across ten completion tasks, task-specific MSCNet achieved mean structural similarity of 0.818 versus 0.798 for the strongest task-matched comparators; matched-capacity analyses showed larger differences in lesion fidelity and boundary preservation. In a blinded 1,000-case reader study, overall image quality met the prespecified non-inferiority criterion for DWI, ADC and T2W completion, but not T1W. In a separate 200-case diagnostic assessment, AUCs for clinically significant cancer were 0.860 with acquired images, 0.841 with MSCNet and 0.797 with baseline-generated images. A locked 186-case three-hospital cohort supported multicentre transportability. These retrospective results support quality-controlled cross-modal reconstruction as an adjunct to acquired prostate MRI.
Unified Multimodal Models aim to achieve any-to-any understanding and generation across arbitrary modalities. However, existing methods primarily rely on modeling implicit statistical correlations and lack cross-modal structural consistency constraints. This deficiency leads to profound issues, including semantic drift, poor compositional generalization, and instability under interventions. In this paper, we propose C3-UniMM, a unified multimodal modeling framework based on Causal Cycle Consistency and Super Alignment. Specifically, we introduce a Structured Latent Causal Graph (SLCG) as a shared cross-modal semantic space and design unified multimodal encoding blocks, enabling understanding and generation to be synergistically optimized within the identical causal semantic structure. Furthermore, we propose a Unified Decoding Space to enforce structural preservation and semantic invertibility during the cross-modal generation process. Theoretical analyses demonstrate that our approach significantly enhances both the invertibility and mechanism invariance of cross-modal mappings. Extensive experimental results across multiple understanding, generation, and compositional generalization tasks indicate that C3-UniMM substantially outperforms existing unified multimodal baselines.
Jingjing Hu, Guo Dan, Haofan Cheng +4cs.CV cs.HC cs.MM
Despite the high accuracy of EEG-based emotion recognition, existing models remain opaque "black boxes", lacking semantic grounding between abstract neural features and human-interpretable states. In this paper, we reframe EEG explainability as a cross-modal generation task, shifting the paradigm from feature attribution to behavioral visualization. We introduce Facial Emoji Proxy Modeling, a novel framework that translates high-dimensional EEG signals into identity-anonymized facial emojis. Guided by the neuroscientific inspiration of neural-facial association, this approach grounds neural representations in the manifold of observable facial dynamics. Technically, our framework integrates FMENet, a specialized backbone modeling expression-relevant spatial synergies, and the Facial Emoji Learning Branch (FELB), which treats emoji reconstruction as a structured semantic regularizer. Extensive experiments on EAV and MMER benchmarks demonstrate that our method achieves state-of-the-art accuracy among EEG-only models. Crucially, it generates semantically faithful facial animations that provide a transparent, privacy-preserving window into the brain's emotional evolution, effectively allowing users to "see the emotion" directly from neural signals. Code is available at https://github.com/xian-sh/SeeEmotion