Chantal REVOL-MULLER

Full Professor — CNU section 61

Welcome to Chantal's Home Page

Full Professor (Professeur des Universités, CNU section 61) at the Department of Telecommunications of the National Institute of Applied Sciences in Lyon (INSA Lyon, France), part of the Université de Lyon.
Member of the Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS, CNRS UMR 5220 – Inserm U1294), Myriad team.

My research addresses medical image processing and deep learning, with a current focus on multimodal generative AI for brain MRI and on the segmentation and longitudinal follow-up of multiple sclerosis lesions.

Distinctions

  • 2025 - RIPEC component 3 scientific excellence award (since 01/10/2025).
  • 2024 - Ordre des Palmes Académiques (14 July 2024 promotion).

Education

  • 2012 - Habilitation to Supervise Research, University C. Bernard Lyon I and INSA Lyon (11/26/12)
    "Integration of multidimensional and heterogeneous data in medical imaging: contributions in fusion, filtering, and segmentation"
    President: F. Peyrin; Reviewers: P. Bolon, M. Garreau, F. Truchetet.
  • 1996 - PhD, University of Saint-Etienne, Image Specialty (11/25/96)
    With highest honors. LISA Laboratory of ICPI / CPE Lyon.
    "Two-dimensional and three-dimensional segmentation methods applied to odontological and biomedical imaging"
    President: M. Druetta; Reviewers: P. Bonton, P. Bolon.
  • 1993 - Engineering Degree in Physics-Electronics, Image Option, ICPI Lyon.
  • 1993 - Master of Advanced Studies (DEA), University of Saint-Étienne – Specialization in Image Processing.

Professional Positions

  • Since 2026 - Full Professor (Professeur des Universités), section 61, INSA Lyon, Department of Telecommunications Engineering, CREATIS (since 01/09/2026).
  • Since 2026 - Co-director of the MUSIC transversal project of CREATIS (Multiple Sclerosis and neuroinflammation, 15 members), jointly with F. Durand-Dubief, neurologist at the Hospices Civils de Lyon.
  • Since 2024 - Elected member of the Telecommunications Department Council (also 2017-2023 and 2011-2013).
  • 2023-2026 - Associate Professor, INSA Lyon, Department of Telecommunications Engineering, CREATIS, Myriad team.
  • 2019-2023 - Head of Internships for 4TC and 5TC, INSA Lyon, Department of Telecommunications Engineering.
  • 2017-2023 - Director of the Apprenticeship Engineer Program, Department of Telecommunications Engineering.
    Deputy Director of the Telecommunications Department.
    Associate Professor, INSA Lyon, Department of Telecommunications Engineering, CITI.
  • 2016-17 - Co-direction of the Apprenticeship Engineer Program, Department of Telecommunications Engineering.
  • 2015-17 - Responsible for the studies of the TC apprenticeship program.
    Associate Professor, section 61, INSA Lyon, Department of Telecommunications Engineering, CITI.
  • 2013-15 - Secondment to secondary education as an Aggregated Mathematics Teacher. Grenoble Academy; George Sand Middle School in La Motte-Servolex; Le Calloud Middle School in La Tour du Pin.
  • 2000-13 - Associate Professor, section 61, INSA Lyon, Department of Telecommunications Engineering, CREATIS.
  • 1999-00 - Temporary Lecturer and Researcher (ATER), University of Grenoble II, IUT Valence, Department of Computer Science, Industrial Systems Computing Option.
  • 1998-99 - Temporary Lecturer and Researcher (ATER), INSA Lyon, Department of Electrical Engineering.
Chantal REVOL-MULLER, Full Professor at INSA Lyon and CREATIS

Summary

I teach signal processing and communication systems, and I conduct research in medical image processing and deep learning.

For eight years I directed the apprenticeship engineer program of the Telecommunications department, and I also served as deputy director of the department and as head of the 4TC and 5TC internship program.

Since 2023 I have refocused my activity on research, with a strong interest in applying deep learning and generative AI to medical imaging, and particularly to brain MRI. My current work follows three directions:

  • Generating synthetic brain MRIs from textual descriptions, in order to enrich training datasets where healthy-subject data are scarce. The longer-term aim is to introduce multiple sclerosis lesions through text prompts, so that clinically realistic pathological cases can be generated for training and validation.
  • Segmenting multiple sclerosis lesions and tracking their progression over time with 3D neural networks, including transformer architectures designed for longitudinal analysis.
  • Analysing microscopy images of insect cells to detect and count symbiotic bacteria using deep learning.

This work is carried out within the Myriad team of CREATIS, in the framework of a doctoral thesis, master's projects and national and international collaborations. It aims to make image analysis more robust, more interpretable, and better adapted to clinical and biological questions.