mlsanityData to 7 Oct 2026

TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes

arXiv 2109.00532 ↗First posted 1 Sep 2021cs.CV cs.LGcited by 8

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Abstract (arXiv)

The longitudinal modeling of neuroanatomical changes related to Alzheimer's disease (AD) is crucial for studying the progression of the disease. To this end, we introduce TransforMesh, a spatio-temporal network based on transformers that models longitudinal shape changes on 3D anatomical meshes. While transformer and mesh networks have recently shown impressive performances in natural language processing and computer vision, their application to medical image analysis has been very limited. To the best of our knowledge, this is the first work that combines transformer and mesh networks. Our results show that TransforMesh can model shape trajectories better than other baseline architectures that do not capture temporal dependencies. Moreover, we also explore the capabilities of TransforMesh in detecting structural anomalies of the hippocampus in patients developing AD.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

15 of 30 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2018Generating 3D faces using Convolutional Mesh Autoencoderscited by 267
  6. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 146
  7. 2018MeshNet: Mesh Neural Network for 3D Shape Representationcited by 95
  8. 2019SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operatorcited by 89
  9. 2018MeshCNN: A Network with an Edgecited by 69
  10. 2018A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshescited by 39
  11. 2018Longitudinal detection of radiological abnormalities with time-modulated LSTMcited by 9
  12. 2019Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detectioncited by 9
3 more
  1. 2019DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorderscited by 6
  2. 2020Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networkscited by 5
  3. 2016A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imagingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 2; 2025: 1; 2026: 1.

8 papers.

  1. 2022Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysiscited by 25
  2. 2024LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshescited by 12
  3. 2023A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening studycited by 3
  4. 2024Multi-scale Spatio-temporal Transformer-based Imbalanced Longitudinal Learning for Glaucoma Forecasting from Irregular Time Series Imagescited by 2
  5. 2022Predicting conversion of mild cognitive impairment to Alzheimer's diseasecited by 1
  6. 2022CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysiscited by 1
  7. 2025Shape Modeling of Longitudinal Medical Images: From Diffeomorphic Metric Mapping to Deep Learningcited by 0
  8. 2026Predicting Brain Morphometry with MT-GNN: Mesh Evolution in Continuous Time with Graph-Based Metric Tensor Embeddingscited by 0