1-year Postdoctoral Position Disentangled and Controllable Latent Representations for Computer Vision and Medical Imaging

Neuroimaging application :
The unsupervised separation of the healthy latent patterns from the pathological ones is not a trivial task in medical imaging. In neuroimaging, pathological brain signatures of psychiatric or neurodevelopmental disorders are not easily visible with the naked eye, even for experienced radiologists. The automatic identification of prognostic brain signatures of clinical courses would pave the way towards personalised medicine in psychiatry. In this project, following our recent works in contrastive analysis (CA), we wish to discover in an unsupervised way the salient imaging patterns that characterize a target dataset of psychiatric patients compared to a control dataset of healthy subjects, as well as what is common between the two domains. Current SOTA methods are based on VAE. However, they all ignore important constraints/assumptions and the generated images have a rather poor quality, typical of VAEs, which decrease their interpretability and usefulness.

Objectives :
– Study and understand the recent advances in disentanglement of latent spaces;
– Review literature on diffusion models with latent spaces;
– Adapt more recent, well-performing models, such as diffusion models, to the CA framework for neuroimaging

PhD in applied mathematics, statistics, computer science, engineering with a good knowledge of Python and deep learning

Applied mathematics, statistics, computer science, engineering, Python, deep learning

Salary will depend on experience and academic background (Starting salary: ~35K euros/year)

Télécom Paris, IPParis, in collaboration with researchers and clinicians from NeuroSpin (CEA)