用原型+强化搜索，让脑网络诊断既准又可解释。
PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
- 用原型学习构建结构化潜在空间，提升模型可解释性。
- 在3个公开数据集上达顶尖准确率，关键脑区与已有研究一致。
- 适合神经影像诊断、AI可解释性研究者参考。
基于fMRI的深度学习诊断方法虽已取得良好准确率，但常受噪声干扰，传统归因方法可靠性不足，可能揭示数据特异性伪影。为此，我们提出PIME框架，通过原型分类与结构扰动的一致性训练，实现内在可解释性与最小充分子图优化的结合。该方法在训练后利用蒙特卡洛树搜索（MCTS）在原型一致目标下提取紧凑的解释性子图。在三个基准fMRI数据集上的实验表明，PIME达到当前最优性能；通过学习到的原型约束搜索空间，所识别的关键脑区与已有神经影像研究高度一致。稳定性分析显示，解释结果在不同脑图谱间具有90%的可复现性。
原文摘要 · Abstract (English)
Recent deep learning methods for fMRI-based diagnosis have achieved promising accuracy by modeling functional connectivity networks. However, standard approaches often struggle with noisy interactions, and conventional post-hoc attribution methods may lack reliability, potentially highlighting dataset-specific artifacts. To address these challenges, we introduce PIME, an interpretable framework that bridges intrinsic interpretability with minimal-sufficient subgraph optimization by integrating prototype-based classification and consistency training with structural perturbations during learning. This encourages a structured latent space and enables Monte Carlo Tree Search (MCTS) under a prototype-consistent objective to extract compact minimal-sufficient explanatory subgraphs post-training. Experiments on three benchmark fMRI datasets demonstrate that PIME achieves state-of-the-art performance. Furthermore, by constraining the search space via learned prototypes, PIME identifies critical brain regions that are consistent with established neuroimaging findings. Stability analysis shows 90% reproducibility and consistent explanations across atlases.
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