![[2602.21046] PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.21046] PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Summary
The paper presents PIME, a novel framework for interpretable brain network analysis using Monte Carlo Tree Search (MCTS) to enhance disorder diagnosis through prototype-based classification.
Why It Matters
PIME addresses the limitations of traditional fMRI analysis methods, which often struggle with noise and reliability. By integrating prototype-based classification with MCTS, it offers a more robust approach to identifying critical brain regions, potentially improving diagnostic accuracy in neuroimaging.
Key Takeaways
- PIME enhances fMRI-based diagnosis accuracy by optimizing subgraph analysis.
- The framework integrates prototype-based classification with MCTS for better interpretability.
- Stability analysis shows 90% reproducibility in findings across different datasets.
Computer Science > Machine Learning arXiv:2602.21046 (cs) [Submitted on 24 Feb 2026] Title:PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis Authors:Kunyu Zhang, Yanwu Yang, Jing Zhang, Xiangjie Shi, Shujian Yu View a PDF of the paper titled PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis, by Kunyu Zhang and 4 other authors View PDF HTML (experimental) Abstract: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 wit...
