Unsupervised anomaly detection in neuroimaging: contributions to representation learning and density support estimation in the latent space
Published in HAL (PhD Defense), 2024
This PhD thesis covers the topic of deep unsupervised anomaly detection (UAD) in neuroimaging. This research is partially grounded on the UAD model that was proposed in [Alaverdyan, MEDIA 2020], whose novelty was to perform the detection step in the latent representation space by adjusting density support model of the normative distribution. This model developed was applied to the detection of subtle (MRI negative) epileptogenic zones in multiparametric MRI and evaluated on a private database. As a first part of this PhD, we optimize the architecture and hyperparameter setting of this UAD model, and evaluate its performance on different open datasets, including the non medical MVTec anomaly detection [Pinon, GRETSI 2023], the WMH challenge, and the Parkinson’s Progression Markers Initiative database [Ramirez, Pinon, MLCN 2021][Pinon, ISBI 2023]. This allows comparison with state of the art UAD methods, especially with the most common methods based on reconstruction error in the image space. The second main phase of this PhD work is to build on the limits of this model [Alaverdyan, MEDIA 2020] and propose original methodological contributions to 1) design patient specific models, relaxing the strong constraint to accurately coregister all control subjects and patients [Pinon, MIDL 2023], 2) provide a probabilistic detection framework to enable ensemble learning and score map uniformization, 3) fuse the representation learning step and the outlier detection step, by proposing a novel deep learning model.
Nicolas Pinon (2024). "Unsupervised anomaly detection in neuroimaging: Contributions to representation learning and density support estimation in the latent space" Doctoral dissertation, INSA Lyon.