NIAgent: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration
Researchers have introduced NIAgent, a novel multi-agent system designed to automate end-to-end neuroimaging analysis, addressing the labor-intensive nature of transforming imaging data into clinical biomarkers. Unlike static workflows such as fMRIPrep, NIAgent employs a code-centric execution paradigm where specialist agents collaboratively synthesize and optimize executable programs. This approach allows for dynamic adaptation to runtime observations and closed-loop decision-making, mimicking human researcher reasoning. The system features a hierarchical verification framework for autonomous quality control, combining cohort-level metric screening with agentic visual inspection to remediate workflow issues. Experimental results on the ADHD-200 and ADNI datasets demonstrate that NIAgent outperforms standard baseline workflows in predictive performance. Furthermore, the system exhibits sophisticated behaviors, including strategy exploration and adaptive refinement, significantly reducing the need for manual trial-and-error parameter tuning. This advancement promises to enhance the scalability and efficiency of clinical biomarker development in neuroscience.
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NIAgent: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration
Researchers have introduced NIAgent, a novel multi-agent system designed to automate end-to-end neuroimaging analysis, addressing the labor-intensive nature of transforming imaging data into clinical biomarkers. Unlike static workflows such as fMRIPrep, NIAgent employs a code-centric execution paradigm where specialist agents collaboratively synthesize and optimize executable programs. This approach allows for dynamic adaptation to runtime observations and closed-loop decision-making, mimicking human researcher reasoning. The system features a hierarchical verification framework for autonomous quality control, combining cohort-level metric screening with agentic visual inspection to remediate workflow issues. Experimental results on the ADHD-200 and ADNI datasets demonstrate that NIAgent outperforms standard baseline workflows in predictive performance. Furthermore, the system exhibits sophisticated behaviors, including strategy exploration and adaptive refinement, significantly reducing the need for manual trial-and-error parameter tuning. This advancement promises to enhance the scalability and efficiency of clinical biomarker development in neuroscience.
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