Modélisation, Apprentissage et Transfert des Représentations Anatomiques en Imagerie Médicale à l'aide de l'IA.

Abstract

Recent advances in computer vision and statistical learning, particularly deep learning, have spurred research in (anatomical) medical imaging in recent years. However, it has been reported that state-of-the-art (SOTA) algorithms from computer vision do not necessarily perform equally well when applied to natural versus medical imaging tasks. Moreover, a simple supervised pre-training using ImageNet—widely employed for natural images—does not always yield optimal results for medical imaging tasks. Given these challenges, my research has focused on developing AI methods tailored to the specific needs and constraints of medical imaging data (e.g., limited data, data biases, lack of labeled data), leveraging clinical knowledge and (healthy) unlabeled data. From a methodological standpoint, my research has followed three main directions: 1) modeling medical knowledge and anatomy, and integrating this into machine learning models; 2) learning compact, relevant, and explanatory representations of anatomical imaging data; and 3) transferring anatomical representations across domains (i.e., different modalities, datasets, and populations) to improve downstream performance or discover new biomarkers. In terms of clinical applications, I have worked exclusively with anatomical data, including MRI and CT scans, focusing on the brain, chest, abdomen, and pelvic regions. In this manuscript, I present some of my work using brain MRI data, based on the three clinical applications I have worked on most extensively. I conclude by discussing future research directions, potential novel clinical applications, and the research valorization of my previous work.

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