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Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces

arXiv 2412.05580 ↗First posted 7 Dec 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose self-supervised masked mesh learning for unsupervised anomaly detection on 3D cortical surfaces. Our framework leverages the intrinsic geometry of the cortical surface to learn a self-supervised representation that captures the underlying structure of the brain. We introduce a masked mesh convolutional neural network (MMN) that learns to predict masked regions of the cortical surface. By training the MMN on a large dataset of healthy subjects, we learn a representation that captures the normal variation in the cortical surface. We then use this representation to detect anomalies in unseen individuals by calculating anomaly scores based on the reconstruction error of the MMN. We evaluated our framework by training on population-scale dataset UKB and HCP-Aging and testing on two datasets of Alzheimer's disease patients ADNI and OASIS3. Our results show that our framework can detect anomalies in cortical thickness, cortical volume, and cortical sulcus characteristics, which are known to be biomarkers of Alzheimer's disease. Our proposed framework provides a promising approach for unsupervised anomaly detection based on normative variation of cortical features.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 97 references that are in the map. First posted on arXiv 7 Dec 2024.

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18 of 97 references.

References · 18

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  3. 2021SimMIM: A Simple Framework for Masked Image Modelingcited by 1,153
  4. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  5. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  6. 2022Diffusion Models for Medical Anomaly Detectioncited by 232
  7. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  8. 2023SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modelingcited by 115
  9. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Modelscited by 97
  10. 2022Masked Image Modeling Advances 3D Medical Image Analysiscited by 72
  11. 2021Anomaly Detection in Medical Imaging -- A Mini Reviewcited by 44
  12. 2022CortexODE: Learning Cortical Surface Reconstruction by Neural ODEscited by 22
6 more
  1. 2022Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detectioncited by 18
  2. 2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Reviewcited by 15
  3. 2021Unsupervised Outlier Detection using Memory and Contrastive Learningcited by 11
  4. 2021Mesh Convolution with Continuous Filters for 3D Surface Parsingcited by 4
  5. 2023Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PETcited by 4
  6. 2023Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mappingcited by 1

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