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

• 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 2024 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: Post-doc
  • CREATIS, INSA Lyon, CNRS, Lyon 1, INSERM
  • Research topic: Weakly supervised learning and domain adaptation for prostate cancer segmentation
  • 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: U-net-like algorithms for motion mask segmentation on chest CT-scans (cancerous and COVID)
  • 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)

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.

Improving motion‐mask segmentation in thoracic CT with multiplanar U‐nets

Ludmilla Penarrubia, Nicolas Pinon, Emmanuel Roux, Eduardo Enrique Dávila Serrano, Jean‐Christophe Richard, Maciej Orkisz, David Sarrut (2022). "Improving motion‐mask segmentation in thoracic CT with multiplanar U‐nets." Medical Physics.

Patch vs. global image-based unsupervised anomaly detection in MR brain scans of early Parkinsonian patients

Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes, Carole Lartizen, Michel Dojat (2021). "Patch vs. global image-based unsupervised anomaly detection in MR brain scans of early Parkinsonian patients" MLCN 2021 (in conjonction with MICCAI).

Stain-free histology to validate quantitative MRI

Gabriel Mangeat, Harris Nami, Nicolas Pinon, Alexandru Foias, Nikola Stikov, Tobias Granberg, Julien Cohen-Adad (2019). "Stain-free histology to validate quantitative MRI" International Society for Magnetic Resonance in Medicine (ISMRM)

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