小样本ViT模型精准识别儿童脑部核磁早期巴顿病征象
TinyViT-Batten: Few-Shot Vision Transformer with Explainable Attention for Early Batten-Disease Detection on Pediatric MRI
- 用小样本学习+知识蒸馏，将大模型压缩为500万参数小模型
- 在79例确诊患儿数据上达到91%准确率，曲线下面积超0.95
- 结合注意力热力图可解释预测，适合临床医生辅助诊断
巴顿病（神经元蜡样脂褐质沉积症）是一种罕见的儿童神经退行性疾病，其早期核磁共振表现细微且常被忽略。本文提出TinyViT-Batten，一种用于从有限训练样本中检测儿科脑部MRI早期巴顿病的小样本视觉变换器框架。通过将大型教师ViT模型知识蒸馏至500万参数的小型模型，并采用基于度量的少样本学习（原型损失，5样本批次），模型在包含79例基因确诊巴顿病患者（27例CLN3来自Hochstein自然史研究，32例CLN2来自国际纵向队列，12例早期表现的CLN2由Cokal等人报告，8例来自Radiopaedia公开数据）及90例年龄匹配对照的多中心数据集上，实现了约91%的准确率和至少0.95的受试者工作特征曲线下面积，优于3D-ResNet与Swin-Tiny基线模型。进一步融合梯度加权类激活映射（Grad-CAM）以突出疾病相关脑区，实现可解释性预测。模型小体积与高性能（敏感性>90%，特异性约90%）表明其具备实际临床应用潜力。
原文摘要 · Abstract (English)
Batten disease (neuronal ceroid lipofuscinosis) is a rare pediatric neurodegenerative disorder whose early MRI signs are subtle and often missed. We propose TinyViT-Batten, a few-shot Vision Transformer (ViT) framework to detect early Batten disease from pediatric brain MRI with limited training cases. We distill a large teacher ViT into a 5 M-parameter TinyViT and fine-tune it using metric-based few-shot learning (prototypical loss with 5-shot episodes). Our model achieves high accuracy (approximately 91%) and area under ROC of at least 0.95 on a multi-site dataset of 79 genetically confirmed Batten-disease MRIs (27 CLN3 from the Hochstein natural-history study, 32 CLN2 from an international longitudinal cohort, 12 early-manifestation CLN2 cases reported by Cokal et al., and 8 public Radiopaedia scans) together with 90 age-matched controls, outperforming a 3D-ResNet and Swin-Tiny baseline. We further integrate Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight disease-relevant brain regions, enabling explainable predictions. The model's small size and strong performance (sensitivity greater than 90%, specificity approximately 90%) demonstrates a practical AI solution for early Batten disease detection.
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