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Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PET

Ravi Hassanaly 1, 2 Simona Bottani 2, 1 Benoît Sauty 1, 2 Olivier Colliot 1, 2 Ninon Burgos 1, 2 
1 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau = Paris Brain Institute
Abstract : Unsupervised anomaly detection using deep learning models is a popular computer-aided diagnosis approach because it does not need annotated data and is not restricted to the diagnosis of a disease seen during training. Such approach consists in first learning the distribution of anomaly free images. Images presenting anomalies are then detected as outliers of this distribution. These approaches have been widely applied in neuroimaging to detect sharp and localized anomalies such as tumors or white matter hyper-intensities from structural MRI. In this work, we aim to detect anomalies from FDG PET images of patients with Alzheimer's disease. In this context, the anomalies can be subtle and difficult to delineate, making the task more difficult and meaning that no ground truth exists to evaluate the approaches. We thus propose a framework to evaluate unsupervised anomaly detection approaches that consists in simulating realistic anomalies from images of healthy subjects. We demonstrate the use of this framework by evaluating an approach based on a 3D variational autoencoder.
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Submitted on : Monday, October 31, 2022 - 11:54:51 AM
Last modification on : Tuesday, November 15, 2022 - 1:37:40 PM


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  • HAL Id : hal-03835015, version 1


Ravi Hassanaly, Simona Bottani, Benoît Sauty, Olivier Colliot, Ninon Burgos. Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PET. SPIE Medical Imaging, Feb 2023, San Diego, United States. ⟨hal-03835015⟩



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