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Fudan University
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Explicitly marking regions of interest in brain MRIs leads to a staggering 37.54% improvement in anomaly detection accuracy, transforming how diagnoses are made.
Aggregating insights from diverse causal discovery experts with LLM-guided reweighting leads to significantly improved causal graph accuracy, even in ambiguous scenarios.
An AI agent can now perform end-to-end echocardiography interpretation with 80% accuracy, rivaling human performance by integrating visual processing, measurement, and expert knowledge.
Achieve automated dental crown design with significantly improved geometric accuracy and clinical feasibility by leveraging margin-aware template deformation and refinement.
ColoDiff generates realistic colonoscopy videos with precise control over clinical attributes, offering a promising solution to data scarcity in intestinal disease diagnosis.
