Intership Scalable Unsupervised Subtle Anomaly

Le descriptif de l’offre ci-dessous est en Anglais_

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Stagiaire de la recherche

**Niveau d'expérience souhaité **:Jeune diplômé

**A propos du centre ou de la direction fonctionnelle**:
The Centre Inria de l’Université de Grenoble groups together almost 600 people in 22 research teams and 7 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE,), but also with key economic players in the area.

The Centre Inria de l’Université Grenoble Alpe is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

**Contexte et atouts du poste**:
**Key words**: Statistical and deep learning, Longitudinal analysis, Clustering, Mixed effect model, variational autoencoders, Biomarkers

Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated and difficult to detect even for experts. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. In the context of Parkinson’s disease and newly diagnosed patients, the detection task is all the more challenging as abnormalities may be subtle and hardly visible in structural MR brain scans. Some preliminary work [Oudoumanessah et al 2023] has shown that structural abnormalities could be detected from MR image data in a way that is consistent with the disease progression, as accounted by the Hoehn and Yahr scale [Hoehn & Yahr 1998].

**Mission confiée**:
The goal of this project is to further improve the reliability of the detection by leveraging additional information coming from longitudinal data. Longitudinal data [Hedeker & Gibbons 2006] consist in the repeated observations of patients over time. In practice, we expect to analyse image data at a few different times corresponding to successive visits of patients. Their analysis informs us on the progression of the disease through the evolution of abnormalities, both in size, numbers, or locations. More specifically, when applied to anomaly detection, the expectation is the confirmation of uncertain detections or the discovery of new ones, not visible at early stages.

Modelling longitudinal data presents different types of challenges. First are the methodological challenges related to the design of relevant models to handle all the data and disease’s characteristics in order to answer the statistical and medical questions. These modelling difficulties cannot be separated from challenges arising from data with very different modalities and time dependencies, in particular involving different acquisition time-sets and different scales of patient screening, resulting on possibly partially missing data [Couronne et al 2019].

Young et al. data [Young et al 2024] recently performed an exhaustive review of data-driven generative models of how a disease evolves over time. Such models use a generative disease progression model and a set of constraints informed by human insight to infer a data-driven disease time axis and the shape of biomarker trajectories along it.

Within this framework, Sauty et al. [Sauty et al 2022] recently investigated a way to model such longitudinal effects directly in the MR images by training a linear mixed effect model in the latent representation space of a longitudinal variational autoencoder. This design enables to combine the robustness of mixed-effects modelling of clinical biomarkers progression with missing data and, for any timepoint, with that of autoencoders both to learn efficient and compact representation of 3D images and reconstruct the image from the latent variable. This model was shown to successfully model based on 3D T1w MRI normal brains and disease progression in Alzheimer patients.

**Principales activités**:
**Directions of research**:

- Review the state-of-the art in the domain of deep generative progression models, e.g. based on the review by Young et al.
- Select and implement some promising models of the literature and try to replicate reported performance on T1w MRI of the ADNI database.
- Compare with standard UAD based on reconstruction error (Baur et al), or support estimation of the normative distribution (Pinon et al)
- Transfer to the study of Parkinson disease progression based on the PPMI and a national cohort (FairPark).
- Train a model on ADNI (and/or PPMI) patients, perform a cluster analysis of the latent trajectory curves, to derive potential subtypes of disease progression. Use this clustering to analyse progression a