Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels. In DASS-21, risk labels are derived from ordered symptom items through fixed item-to-subscale mappings. We propose \textbf{DynaBridge}, a dynamic summary-guided cross-task multimodal framework for DASS-structured mental health assessment. DynaBridge encodes acoustic, visual, and textual cues across multiple sessions and augments them with frozen-LLM-generated DASS-aware summaries as participant-level semantic evidence. It predicts ordinal item distributions, reconstructs depression, anxiety, and stress risk evidence from item-level soft scores, and fuses this evidence with direct multimodal risk predictions. A confidence-aware refinement strategy further incorporates high-confidence semantic cues conservatively. On the official AdoDAS validation split, DynaBridge outperforms the official baseline and representative multimodal methods, achieving 0.5012 mean F1 for D/A/S risk prediction and 0.3216 mean QWK for DASS-21 item prediction. These results show the value of bridging multimodal cues, semantic summaries, and DASS-21 psychometric structure.
Shiyu Teng, H. Yu, Jiaqing Liu et al.· 0 citations
Precise delineation of lesion boundaries is a cornerstone of radiological image analysis, directly impacting downstream prognostic accuracy. However, manual annotation of 3D volumetric data is labor-intensive and expert-dependent, creating a significant bottleneck in clinical workflows. While multimodal text-guided segmentation has gained traction, glioma research remains constrained by data scarcity and strict ethical regulations, resulting in a lack of integrated datasets and domain-specific methodologies. To bridge this gap, we introduce TextBraTS, a high-quality, open-access dataset featuring aligned 3D MRI, expert-verified textual descriptions, and genotype information. We establish a Data-Centric Paradigm encompassing an LLM-driven Clinical Narrative Standardization Protocol that distills unstructured reports into structured semantic priors. Furthermore, we propose a Multi-level Gated Fusion (MGF) network to dynamically integrate these expert-curated semantic attributes into 3D vision backbones. Benchmarked against state-of-the-art methods, our framework demonstrates superior precision in both glioma segmentation and molecular subtyping. This work quantifies the "Semantic Gain" of structured textual priors as a proof-of-concept, providing a controlled benchmark for the potential of structured text guidance and providing a robust foundation for integrated radiogenomic analysis and computer-aided diagnosis research.
Xiaoyu Shi, Rahul Kumar Jain, Yinhao Li et al.· IEEE journal of biomedical a...· 0 citations
