# TinyViT-Batten: Few-Shot Vision Transformer with Explainable Attention for Early Batten-Disease Detection on Pediatric MRI

arXiv: https://arxiv.org/abs/2510.09649 · HTML: https://mlsanity.com/paper/2510.09649/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [TinyViT: Fast Pretraining Distillation for Small Vision Transformers](https://mlsanity.com/paper/2207.10666/) (2022)

## Abstract (arXiv)

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.