Description du projet
Une nouvelle méthode d’apprentissage automatique pour l’imagerie spectrale
Les techniques d’imagerie spectrale utilisent la lumière et le son pour mesurer de manière non invasive les propriétés optiques des tissus biologiques. Leur sécurité et leur faible coût les rendent idéales pour diverses applications sanitaires. Toutefois, des décennies de recherches portant sur la résolution du problème inverse consistant à reconstruire les propriétés de tissus cliniquement pertinents à partir de mesures spectrales n’ont pas apporté de solutions capables de quantifier les paramètres des tissus de manière fiable dans un cadre clinique. Le projet NEURAL SPICING, financé par l’UE, s’attaque à ce problème de décodage d’informations par le biais de réseaux neuronaux limités par la physique. Les chercheurs utilisent une approche moderne d’apprentissage par simulation pour générer des images spectrales simulées et réalistes à partir de données faiblement marquées ou non marquées, qui peuvent ensuite éclairer l’entraînement d’algorithmes. Cette nouvelle approche est susceptible d’apporter des innovations en matière de diagnostics et de traitements pour une variété de maladies.
Objectif
Had we the capacity to image the molecular tissue composition in real time, without exposing the patient or clinical staff to radiation, then this would revolutionize healthcare. Spectral imaging techniques, such as multispectral diffuse reflectance imaging and photoacoustics, have the potential to recover important tissue properties including oxygenation, temperature and the concentration of water. However, decades of research invested in solving the inverse problem of reconstructing clinically relevant tissue properties from spectral measurements have failed to produce solutions that can quantify tissue parameters robustly in a clinical setting. Initial attempts to address the limitations of model-based approaches with machine learning were hampered by the absence of labeled reference data needed for supervised algorithm training. While training data simulation can potentially address this bottleneck, the domain gap between real and simulated images remains a huge unsolved challenge.
This project at the intersection of physics, biology, medicine, and data science bridges the domain gap with a “learning-to-simulate” approach and reframes the quantification problem as a decoding problem that can be tackled with physics-constrained neural networks. It is based on the hypothesis that unlabeled and weakly labeled measurement data can be leveraged to improve the realism of simulated spectral images and ultimately the quantification accuracy. In building upon modern machine learning techniques, the concept naturally allows different sources of uncertainty to be handled and even exploited, requires no labeled data for the target task, and enables imaging systems to learn from their experience. The proposed second generation of spectral imaging is inherently safe and low-cost and could thus pave the way for a new era in interventional healthcare, with clinical applications ranging from cancer diagnosis to the staging and therapy of cardiovascular and inflammatory diseases.
Champ scientifique
- natural sciencescomputer and information sciencesdata science
- medical and health scienceshealth sciencesinflammatory diseases
- medical and health sciencesclinical medicineoncology
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Mots‑clés
Programme(s)
Régime de financement
ERC-COG - Consolidator GrantInstitution d’accueil
69120 Heidelberg
Allemagne