Problem
Research questions and friction points this paper is trying to address.
brain-age prediction
predictive accuracy
reproducible mechanisms
neurobiologically supported mechanisms
post-hoc explanation methods
XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction
🤖 AI Summary
为解决脑龄预测模型依赖机制的可解释性问题，提出XAI-Refine方法，通过自动化解释-知识循环来提高模型的神经生物学支持度和预测准确性。
📝 Abstract
Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reproducible or neurobiologically supported mechanisms. Post-hoc explanation methods can expose these mechanisms, but existing workflows typically stop at diagnosis or require correction targets to be specified before model analysis. We propose XAI-Refine, an automated explanation-knowledge loop for brain-age prediction from resting-state functional connectivity. At each iteration, XAI-Refine consolidates complementary post-hoc analyses across repeated training runs into reliable, structured model explanations. It converts each reliable explanation into a neutral neurobiological question, retrieves and verifies relevant literature, and compiles the verified evidence into an admissible set in the same typed explanation space. The target for refinement is defined as the minimal projection of the current model explanation onto the admissible set induced by applicable verified knowledge. This revised explanation is then translated into a differentiable constraint while preserving the originating model variable, measurement operator, and applicable scope. Candidate updates are promoted only when multi-seed validation confirms target-directed explanatory movement, predictive performance remains within a prespecified guardrail, and non-target explanatory drift remains bounded. Experiments on functional-connectivity-based brain-age prediction evaluate predictive performance, explanation reliability, literature alignment, and target-specific model revision, illustrating a structured route from post-hoc analysis to evidence-guided model refinement.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automated Explanation-Knowledge Loop
Post-hoc Analysis
Brain-age Prediction
Resting-state Functional Connectivity
Evidence-guided Model Refinement
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