A PDF, more detailed version (08/2025) is also available here

Education

International version

Ph.D in Signal and Image Processing, INSA Lyon, 2020-2024
M.S in Mathematics for vision and machine learning, ENS Paris-Saclay, 2019-2020
B.S. and M.S. in Electrical Engineering, ENS Paris-Saclay (scholarship), 2016-2020
Technical diploma in Physical Measurements, Strasbourg University (valedictorian), 2014-2016

French version

Qualification MCF en section CNU 61 et 27, 2025
Doctorat en traitement du signal et des images, INSA Lyon, 2020-2024
Master 2 Mathématiques Vision Apprentissage (MVA), ENS Paris-Saclay, 2019-2020
Diplôme de l’ENS Paris-Saclay, Electronique Electrotechnique et Automatique (EEA), élève normalien, 2016-2020
DUT Mesures physiques, Université de Strasbourg (major), 2014-2016

Service

2025: Reviewer for the MIDL 2025 conference
2025: Reviewer for TMI
2024: Volunteer at EUSIPCO 2024
2024: Reviewer for TMI
2024: Reviewer for the MIDL 2024 conference (awarded outstanding reviewer)
2021-2024: Elected student at the laboratory council
Spring 2023: Co-supervisor of Guillaume Perna master’s internship on probability calibration and SVM methods for anomaly detection in neuroimaging.
Spring 2022: Co-supervisor of Robin Trombetta master’s internship on self-supervised representation learning for anomaly detection in industrial images and for Parkinson’s disease.
2021-2024: Core member of LIFE (Laboratory Initiative For Environment)
2021-2023: Jury for admission in engineering school (INSA Lyon)
2023: Scientific dissemination among primary school students about sound waves (Atelier “Trace ton son”)
2022-2023: Internship tutor for 4 undergrad students (stage ouvrier)

Work experience

2024: Research engineer
- CREATIS, INSA Lyon, CNRS, Lyon 1, INSERM
- Research topic: Hybridization of prostate cancer detection models
- Supervisor: Carole Lartizien
2020-2024: PhD student
- CREATIS, INSA Lyon, CNRS, Lyon 1, INSERM
- Thesis: Unsupervised anomaly detection in neuroimaging: contributions to representation learning and density support estimation in the latent space
- Supervisor: Carole Lartizien
Spring 2020: Research intern
- CREATIS, INSA Lyon, CNRS, Lyon 1, INSERM
- Research topic: Multi-planar U-Net for motion mask segmentation on abdominal CT scans (cancerous and COVID-19 cases)
- Supervisors: Emmanuel Roux, Maciej Orkisz
2018-2019: Research intern
- Neuropoly, Polytechnique Montréal
- Research topic: Automatic detection of symmetry axes in spinal cord MRI to improve registration robustness
- Supervisor: Julien Cohen-Adad
Spring 2018: Research intern
- LTCI, Telecom Paris
- Research topic: Automatic segmentation of ovaries in MRI (in collaboration with a radiologist)
- Supervisors: Isabelle Bloch, Pietro Gori

Publications (details here)

OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection

Nicolas Pinon, Carole Lartizien (2025). "OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection." Preprint, submitted to IEEE Transaction on Image Processing.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models

Daria Zotova, Nicolas Pinon, Robin Trombetta, Romain Bouet, Julien Jung, Carole Lartizien (2025). "GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models." Computer Methods and Programs in Biomedicine.

Unsupervised anomaly detection in neuroimaging: contributions to representation learning and density support estimation in the latent space

Nicolas Pinon (2024). "Unsupervised anomaly detection in neuroimaging: Contributions to representation learning and density support estimation in the latent space" Doctoral dissertation, INSA Lyon.

Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis

Nicolas Pinon, Robin Trombetta, Carole Lartizien (2023). "Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis" XXIXème Colloque Francophone de Traitement du Signal et des Images.

One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities

Nicolas Pinon, Robin Trombetta, Carole Lartizien (2023). "One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities." Medical Imaging with Deep Learning.

Brain subtle anomaly detection based on auto-encoders latent space analysis: application to de novo parkinson patients

Nicolas Pinon, Geoffroy Oudoumanessah, Robin Trombetta, Michel Dojat, Florence Forbes, Carole Lartizien (2023). "Brain subtle anomaly detection based on auto-encoders latent space analysis: application to de novo parkinson patients." IEEE 20th International Symposium on Biomedical Imaging.

Teaching (details here)

Image processing
[16h] Cours intégré niveau L2, INSA Lyon FIMI, 2024-2024

Algorithms and programming
[35h] TD niveau L1, INSA Lyon FIMI, 2022-2023

Machine learning and Deep learning
[8h] TP niveau M2, CPE Lyon, 2022-2023

Image processing projects
[16h] TP niveau L2, INSA Lyon FIMI, 2020-2022

Electromagnetism and waves
[83h] TD niveau L2, INSA Lyon FIMI, 2020-2022

Electricity and electromagnetism
[23h] TP niveau L2, INSA Lyon FIMI, 2020-2022

Deep learning for medical imaging
[12h] École de printemps, DLMI Spring school, 2020-2022

Mentorship for middle school students (Maths, Physics)
[20h] Mentorat, Collège Victor Hugo, Cachan (Coordées de la réussite), 2017-2018