# Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs

arXiv: https://arxiv.org/abs/2302.00901 · HTML: https://mlsanity.com/paper/2302.00901/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://mlsanity.com/paper/2103.10697/) (2021)
- [How Do Vision Transformers Work?](https://mlsanity.com/paper/2202.06709/) (2022)
- [Vision Transformer with Deformable Attention](https://mlsanity.com/paper/2201.00520/) (2022)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing](https://mlsanity.com/paper/2301.04558/) (2023)
- [SeqFormer: Sequential Transformer for Video Instance Segmentation](https://mlsanity.com/paper/2112.08275/) (2021)
- [Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification](https://mlsanity.com/paper/1701.06643/) (2017)
- [Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification](https://mlsanity.com/paper/1803.02544/) (2018)
- [Reinforcement Learning based Disease Progression Model for Alzheimer's Disease](https://mlsanity.com/paper/2106.16187/) (2021)
- [Fast Predictive Simple Geodesic Regression](https://mlsanity.com/paper/1711.05766/) (2017)

## Built on by (most cited first, 12 of 12)

- [Transformer Model for Alzheimer's Disease Progression Prediction Using Longitudinal Visit Sequences](https://mlsanity.com/paper/2507.03899/) (2025)
- [Assessing the significance of longitudinal data in Alzheimer's Disease forecasting](https://mlsanity.com/paper/2405.17352/) (2024)
- [CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection](https://mlsanity.com/paper/2505.06903/) (2025)
- [GLOMIA-Pro: A Generalizable Longitudinal Medical Image Analysis Framework for Disease Progression Prediction](https://mlsanity.com/paper/2507.12500/) (2025)
- [LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments](https://mlsanity.com/paper/2603.12071/) (2026)
- [Parcel2Progression: An Anatomy-aware Longitudinal Framework for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2608.08753/) (2026)
- [Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis](https://mlsanity.com/paper/2504.07775/) (2025)
- [SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2507.16267/) (2025)
- [An Enhanced Pyramid Feature Network Based on Long-Range Dependencies for Multi-Organ Medical Image Segmentation](https://mlsanity.com/paper/2509.24358/) (2025)
- [Multimodal Visual Surrogate Compression for Alzheimer's Disease Classification](https://mlsanity.com/paper/2601.21673/) (2026)
- [A Two-Scan Deep Learning Model for Predicting Dementia in Mild Cognitive Impairment](https://mlsanity.com/paper/2605.28397/) (2026)
- [DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction](https://mlsanity.com/paper/2606.09140/) (2026)

## Abstract (arXiv)

Structural magnetic resonance imaging (sMRI) is widely used for brain neurological disease diagnosis; while longitudinal MRIs are often collected to monitor and capture disease progression, as clinically used in diagnosing Alzheimer's disease (AD). However, most current methods neglect AD's progressive nature and only take a single sMRI for recognizing AD. In this paper, we consider the problem of leveraging the longitudinal MRIs of a subject for AD identification. To capture longitudinal changes in sMRIs, we propose a novel model Longformer, a spatiotemporal transformer network that performs attention mechanisms spatially on sMRIs at each time point and integrates brain region features over time to obtain longitudinal embeddings for classification. Our Longformer achieves state-of-the-art performance on two binary classification tasks of separating different stages of AD using the ADNI dataset. Our source code is available at https://github.com/Qybc/LongFormer.