Post-doc FNS
University of Lausanne (UNIL) and Lausanne University Hospital (CHUV)
Affiliations
University of Lausanne, Center for Biomedical Imaging (CIBM)
Lausanne University Hospital, Radiology Department (CHUV)
Contact
Email: thomas (dot) sanchez (at) unil (dot) ch
Postal address: Centre de Recherche en Radiologie (PET03), Rue du Bugnon 46, CH-1011 Lausanne
Office: Rue Pépinet 3, 2nd floor, CH-1003 Lausanne
Website: https://t-sanchez.github.io/
Google Scholar: https://scholar.google.com/citations?user=lpL7C1YAAAAJ&hl=fr
Research interests
Thomas’ research at MIAL focuses on fetal brain MRI. His aim is to build robust reconstruction and analysis pipelines. He develops protocols and automated methods for quality control, and works as well on reconstruction and segmentation problems.
His research interests revolve around the application of deep learning to medical imaging, in particular MRI. He cares a lot about making deep learning models more robust, either by design or through more robust validation procedures. He is also broadly interested by generative models, inverse problems, variational inference and experiment design.
Short Bio
Thomas obtained from EPFL his master degree in Computational Science and Engineering in 2018 as well as his PhD in Computer Science in 2022. In his thesis, he focused on optimizing Cartesian sampling masks for accelerated MRI, with the goal of achieving the best image quality with the shortest acquisition time. In this context, he worked with both classical and deep learning methods for reconstruction, as well as reinforcement learning approaches for optimizing sampling masks. In 2022, Thomas joined the Medical Image Analysis Laboratory (MIAL) as a postdoctoral researcher. He works on deep learning-based quality control of fetal brain MRI as well as 3D super-resolution reconstruction.