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Chongqing Medical University

Academic institutionasia · cn
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Research library4linked papers
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Selected work

Representative Papers

ConsultMind:Towards Automated Diagnostic Consultation via Uncertainty-Aware Reasoning

Sep 25, 2026

This study addresses the challenges of disconnected open-ended questioning from Bayesian networks and insufficient decision interpretability in automated medical consultation by proposing an uncertainty-aware framework. Methodologically, it introduces a novel automated pipeline that integrates heterogeneous clinical narratives to construct a Disease-Symptom Bayesian Network (DSBN). By combining large language model-assisted knowledge extraction with uncertainty-quantified reasoning, the framework drives adaptive inquiry and diagnostic decision-making through dynamic posterior probability updates. Experimental results demonstrate that the proposed approach improves Top-1 and Top-3 diagnostic accuracy by over 20%. Furthermore, physician evaluations confirm that its explanation quality and diagnostic plausibility significantly surpass those of existing baselines.

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M$^3$R-Bench: A Unified Benchmark for Evidence-Grounded Multimodal Metaphor Understanding

Aug 06, 2026

This work addresses the limitation of existing metaphor understanding benchmarks, which predominantly rely on isolated subtasks and lack evaluation of cross-modal target–source mappings grounded in joint visual and textual evidence. To bridge this gap, we introduce M³R-Bench, a unified multimodal benchmark grounded in Conceptual Metaphor Theory, comprising 1,000 human-verified image–text samples annotated across four layers: metaphor existence, mapping relations, sentiment polarity, and stepwise explanations. We further propose a novel three-stage evaluation framework—evidence identification, mapping construction, and sentiment inference—that reveals current models’ overreliance on textual cues and neglect of visual evidence. Building upon this, we develop M³R-Reasoner, which integrates curriculum-based reasoning supervision with task-aware reinforcement learning to guide multimodal large language models toward evidence–mapping consistent reasoning. Despite using only an 8B-parameter backbone, our model surpasses larger closed-source counterparts across all four metrics, outscoring GPT-5.5 by 28.45 and 30.11 points in visual evidence and sentiment plausibility, respectively, and exceeding Claude-Sonnet-4.6 by an average of 8.00 points.

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Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement

Aug 04, 2026

This work addresses radiomic distortion and spatial misalignment in synthetic contrast-enhanced breast MRI, which arise from generator intensity upper-bound constraints and independent intensity scaling between source and target images. To resolve these issues, the authors propose a Predictive Enhancement Calibration (PEC) method that establishes a case-adaptive shared coordinate system and predicts the missing enhancement upper bound directly from pre-contrast images during inference. PEC leverages a pretrained FLUX latent flow model for efficient conditional generation, incorporating parameter-efficient reference conditioning, target round-trip reconstruction, and a unified coordinate strategy within a single training framework. Evaluated on the MAMA100 cohort under a source-only setting, PEC significantly improves all eight assessment metrics, with the most pronounced gains observed in MSE and LPIPS.

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Recent publications

Latest Papers

ConsultMind:Towards Automated Diagnostic Consultation via Uncertainty-Aware Reasoning

Sep 25, 2026

This study addresses the challenges of disconnected open-ended questioning from Bayesian networks and insufficient decision interpretability in automated medical consultation by proposing an uncertainty-aware framework. Methodologically, it introduces a novel automated pipeline that integrates heterogeneous clinical narratives to construct a Disease-Symptom Bayesian Network (DSBN). By combining large language model-assisted knowledge extraction with uncertainty-quantified reasoning, the framework drives adaptive inquiry and diagnostic decision-making through dynamic posterior probability updates. Experimental results demonstrate that the proposed approach improves Top-1 and Top-3 diagnostic accuracy by over 20%. Furthermore, physician evaluations confirm that its explanation quality and diagnostic plausibility significantly surpass those of existing baselines.

0 citationsRead paper

M$^3$R-Bench: A Unified Benchmark for Evidence-Grounded Multimodal Metaphor Understanding

Aug 06, 2026

This work addresses the limitation of existing metaphor understanding benchmarks, which predominantly rely on isolated subtasks and lack evaluation of cross-modal target–source mappings grounded in joint visual and textual evidence. To bridge this gap, we introduce M³R-Bench, a unified multimodal benchmark grounded in Conceptual Metaphor Theory, comprising 1,000 human-verified image–text samples annotated across four layers: metaphor existence, mapping relations, sentiment polarity, and stepwise explanations. We further propose a novel three-stage evaluation framework—evidence identification, mapping construction, and sentiment inference—that reveals current models’ overreliance on textual cues and neglect of visual evidence. Building upon this, we develop M³R-Reasoner, which integrates curriculum-based reasoning supervision with task-aware reinforcement learning to guide multimodal large language models toward evidence–mapping consistent reasoning. Despite using only an 8B-parameter backbone, our model surpasses larger closed-source counterparts across all four metrics, outscoring GPT-5.5 by 28.45 and 30.11 points in visual evidence and sentiment plausibility, respectively, and exceeding Claude-Sonnet-4.6 by an average of 8.00 points.

0 citationsRead paper

Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement

Aug 04, 2026

This work addresses radiomic distortion and spatial misalignment in synthetic contrast-enhanced breast MRI, which arise from generator intensity upper-bound constraints and independent intensity scaling between source and target images. To resolve these issues, the authors propose a Predictive Enhancement Calibration (PEC) method that establishes a case-adaptive shared coordinate system and predicts the missing enhancement upper bound directly from pre-contrast images during inference. PEC leverages a pretrained FLUX latent flow model for efficient conditional generation, incorporating parameter-efficient reference conditioning, target round-trip reconstruction, and a unified coordinate strategy within a single training framework. Evaluated on the MAMA100 cohort under a source-only setting, PEC significantly improves all eight assessment metrics, with the most pronounced gains observed in MSE and LPIPS.

0 citationsRead paper