PIME: 基于原型的可解释MCTS增强脑网络分析用于疾病诊断
PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
AI总结：
PIME通过整合原型分类和一致性训练，利用MCTS提取紧凑的最小充分解释子图，实现脑网络分析的可解释性与高准确性。
摘要：
最近深度学习方法在基于fMRI的诊断中通过建模功能连接网络实现了有希望的准确性。然而，标准方法往往难以处理噪声交互，而传统事后归因方法可能缺乏可靠性，可能突出数据集特定的伪影。为了解决这些挑战，我们引入了PIME，一个可解释框架，通过将内在可解释性与最小充分子图优化相结合，整合了基于原型的分类和一致性训练，通过在学习过程中引入结构扰动。这鼓励了一个结构化的潜在空间，并在原型一致的目标下启用蒙特卡洛树搜索（MCTS）以在训练后提取紧凑的最小充分解释子图。在三个基准fMRI数据集上的实验表明，PIME实现了最先进的性能。此外，通过约束搜索空间，PIME识别出与已建立的神经影像学发现一致的关键脑区。稳定性分析显示90%的可重复性和在不同图谱中的一致解释。
英文摘要：
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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