mlsanityData to 14 Oct 2026

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

arXiv 2401.16363 ↗First posted 29 Jan 2024eess.IV cs.CVcited by 10

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Abstract (arXiv)

Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity. This approach has the great advantage of not requiring tedious pixel-wise data annotation and offers possibility to generalize to any kind of anomalies, including that corresponding to rare diseases. By training a deep generative model with only images from healthy subjects, the model will learn to reconstruct pseudo-healthy images. This pseudo-healthy reconstruction is then compared to the input to detect and localize anomalies. The evaluation of such methods often relies on a ground truth lesion mask that is available for test data, which may not exist depending on the application. We propose an evaluation procedure based on the simulation of realistic abnormal images to validate pseudo-healthy reconstruction methods when no ground truth is available. This allows us to extensively test generative models on different kinds of anomalies and measuring their performance using the pair of normal and abnormal images corresponding to the same subject. It can be used as a preliminary automatic step to validate the capacity of a generative model to reconstruct pseudo-healthy images, before a more advanced validation step that would require clinician's expertise. We apply this framework to the reconstruction of 3D brain FDG PET using a convolutional variational autoencoder with the aim to detect as early as possible the neurodegeneration markers that are specific to dementia such as Alzheimer's disease.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 38 of its 94 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

38 of 94 references.

References · 38

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26 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 2.

10 papers.

  1. 2025CF-Seg: Counterfactuals meet Segmentationcited by 10
  2. 2025Applications of Large Models in Medicinecited by 6
  3. 2025Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's diseasecited by 1
  4. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  5. 2026Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detectioncited by 1
  6. 2024IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Studycited by 0
  7. 2024Tau Anomaly Detection in PET Imaging via Bilateral-Guided Deterministic Diffusion Modelcited by 0
  8. 2025SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Imagescited by 0
  9. 2025MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillationcited by 0
  10. 2026Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Priorcited by 0