PHD student (2016 – )
Supervisors: Olivier Saut, Baudouin Denis de Senneville
Software engineer (2014 – 2016)
- Medical images processing and analysis
- Radiomics : evolution of morphological and textural features of tumors
- Machine/deep learning for prediction (grade of tumor, response to treatment, relapse …)
- 2014 – 2016: Software engineer for MONC team. Development of the papriK python library (toolbox for medical images processing) with continuous integration on gitlab and CI Inria.
- 2014: 6 months internship in EPFL (Lausanne). Agent based modeling of fish shoals behavior.
- 2016-now: Doctoral training (data sciences, AI, ethics)
- 2014: Master’s degree in Bioinformatics
- 2012: BS degree in Biology of Organisms and Ecosystems
Since 2017 ENSEIRB-MATMECA (INP Bordeaux):
- C++ for numerical computation (Matmeca, 2nd year): see Annabelle Collin’s webpage
- C++ programming (Computer Science, 2nd year): see Julien Allali’s webpage
Spring 2014 at EPFL:
- Crombé* A., Périer* C. et al. T2-based MRI Delta-Radiomics Improve Response Prediction in Soft-Tissue Sarcomas Treated by Neoadjuvant Chemotherapy. European Radiology, , Volume 28, Issue 7, pp 2801–2811, [*co-first authors]
- Hocquelet A., Auriac T., Périer C. et al. (2018, Feb.) Pre-treatment magnetic resonance-based texture features as potential imaging biomarkers for predicting event free survival in anal cancer treated by chemoradiotherapy. Eur. Radiology 28(7):2801-2811
+ reviewer for British Journal of Radiology Open
- Workshop HTE, Paris Nov. 2018. Radiomics to improve prediction of response in soft-tissue sarcomas treated by chemotherapy.
- Journées Francophones de Radiologie, Paris Oct. 2018. T2-based MRI-radiomics to improve prediction of histologic response in soft-tissue sarcomas treated by neoadjuvant chemotherapy – preliminary results.
- Workshop Cancéropôle Grand Ouest, Le Bono, Sep. 2017. Textural analysis of pancreatic cancer during radiotherapy and machine learning.