mlsanityData to 14 Oct 2026

Brain PathoGraph Learning

arXiv 2509.21742 ↗First posted 26 Sep 2025cs.LG cs.AIcited by 0

On the map: Knowledge distillation → Distillation for medical imaging

Abstract (arXiv)

Brain graph learning has demonstrated significant achievements in the fields of neuroscience and artificial intelligence. However, existing methods struggle to selectively learn disease-related knowledge, leading to heavy parameters and computational costs. This challenge diminishes their efficiency, as well as limits their practicality for real-world clinical applications. To this end, we propose a lightweight Brain PathoGraph Learning (BrainPoG) model that enables efficient brain graph learning by pathological pattern filtering and pathological feature distillation. Specifically, BrainPoG first contains a filter to extract the pathological pattern formulated by highly disease-relevant subgraphs, achieving graph pruning and lesion localization. A PathoGraph is therefore constructed by dropping less disease-relevant subgraphs from the whole brain graph. Afterwards, a pathological feature distillation module is designed to reduce disease-irrelevant noise features and enhance pathological features of each node in the PathoGraph. BrainPoG can exclusively learn informative disease-related knowledge while avoiding less relevant information, achieving efficient brain graph learning. Extensive experiments on four benchmark datasets demonstrate that BrainPoG exhibits superiority in both model performance and computational efficiency across various brain disease detection tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. Builds on 19 of its 46 references that are in the map. First posted on arXiv 26 Sep 2025.

Builds on

19 of 46 references.

References · 19

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  4. 2022Brain Network Transformercited by 94
  5. 2021Graph Neural Networks in Network Neurosciencecited by 92
  6. 2021Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attentioncited by 87
  7. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 75
  8. 2022Spectral Feature Augmentation for Graph Contrastive Learning and Beyondcited by 49
  9. 2021LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interfacecited by 36
  10. 2023NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomicscited by 33
  11. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  12. 2025Long-range Brain Graph Transformercited by 12
7 more
  1. 2023Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networkscited by 11
  2. 2023Learning High-Order Relationships of Brain Regionscited by 10
  3. 2025Biologically Plausible Brain Graph Transformercited by 10
  4. 2024A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learningcited by 9
  5. 2022Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Modelcited by 8
  6. 2025BrainOOD: Out-of-distribution Generalizable Brain Network Analysiscited by 7
  7. 2025BrainMAP: Multimodal Graph Learning For Efficient Brain Disease Localizationcited by 2

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  2. 2024BrainMAP: Learning Multiple Activation Pathways in Brain Networks
  3. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
  4. 2023BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model
  5. 2025Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
  6. 2024Graph Residual Noise Learner Network for Brain Connectivity Graph Prediction
  7. 2022Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation
  8. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection