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.
Deep learning · Pseudo-healthy reconstruction · Unsupervised anomaly detection · Variational autoencoder · 3D PET · Alzheimer's disease
@article{melba:2024:003:hassanaly,
title = "Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET",
author = "Hassanaly, Ravi and Brianceau, Camille and Solal, Maëlys and Colliot, Olivier and Burgos, Ninon",
journal = "Machine Learning for Biomedical Imaging",
volume = "2",
issue = "Special Issue for Generative Models",
year = "2024",
pages = "611--656",
issn = "2766-905X",
doi = "https://doi.org/10.59275/j.melba.2024-b87a",
url = "https://melba-journal.org/2024:003"
}
TY - JOUR
AU - Hassanaly, Ravi
AU - Brianceau, Camille
AU - Solal, Maëlys
AU - Colliot, Olivier
AU - Burgos, Ninon
PY - 2024
TI - Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
T2 - Machine Learning for Biomedical Imaging
VL - 2
IS - Special Issue for Generative Models
SP - 611
EP - 656
SN - 2766-905X
DO - https://doi.org/10.59275/j.melba.2024-b87a
UR - https://melba-journal.org/2024:003
ER -