通过可学习边掩码保留脑图结构语义，提升小样本精神疾病诊断准确率
Structure Matters: Brain Graph Augmentation via Learnable Edge Masking for Data-efficient Psychiatric Diagnosis
- 用可学习边掩码捕捉脑图关键结构语义，避免传统增强破坏重要连接
- 在小标注数据下表现优于现有方法，跨两个真实数据集均显著提升精度
- 揭示临床相关连接模式，适合关注可解释性精神疾病诊断的研究者
标注脑网络数据的稀缺性使得实现精准且可解释的精神疾病诊断面临挑战。自监督学习（SSL）虽具前景，但现有方法常采用破坏脑图结构语义的增强策略。为此，我们提出SAM-BG，一种两阶段脑图表征学习框架，以保持结构语义。预训练阶段，基于少量标注子集训练边掩码器以捕获关键结构语义；在SSL阶段，提取的结构先验指导结构感知增强，使模型学习更具语义意义和鲁棒的表示。在两个真实世界精神疾病数据集上的实验表明，SAM-BG在小标注数据设置下优于现有最先进方法，并揭示了具有临床意义的连接模式，增强了可解释性。代码已公开于https://github.com/mjliu99/SAM-BG。
原文摘要 · Abstract (English)
The limited availability of labeled brain network data makes it challenging to achieve accurate and interpretable psychiatric diagnoses. While self-supervised learning (SSL) offers a promising solution, existing methods often rely on augmentation strategies that can disrupt crucial structural semantics in brain graphs. To address this, we propose SAM-BG, a two-stage framework for learning brain graph representations with structural semantic preservation. In the pre-training stage, an edge masker is trained on a small labeled subset to capture key structural semantics. In the SSL stage, the extracted structural priors guide a structure-aware augmentation process, enabling the model to learn more semantically meaningful and robust representations. Experiments on two real-world psychiatric datasets demonstrate that SAM-BG outperforms state-of-the-art methods, particularly in small-labeled data settings, and uncovers clinically relevant connectivity patterns that enhance interpretability. Our code is available at https://github.com/mjliu99/SAM-BG.
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