PhD position funded by an ANR grant
Scientific fields : machine learning, deep learning, multimodality imaging, clinical decision systems
Please follow the link for a detailed description of the project: https://www.creatis.insa-lyon.fr/site7/fr/node/46640
Scientific and work environment : The doctoral position will take place at the CREATIS laboratory (www.creatis.insa-lyon.fr) in Lyon.
The succesful candidate will join the ’Image and Models’ team (https://www.creatis.insa-lyon.fr/site7/en/Images_Models)
CREATIS has developed strong skills in developing clinical decision systems (CAD) for cancer [1, 2, 3, 4] and brain imaging [5, 6, 7] based on the most advanced machine learning techniques. Such systems are designed toassist clinicians in their diagnosis by highlighting abnormal regions in an image. One active project of the ‘Images and Models’ team concernsthe prototyping of a computer-aided diagnosis system for prostate cancer screening based on multiparametric magnetic resonance imaging (MRI).
Despite an important improvement brought by such systems for the problem of cancer mapping, they still suffer from limitations that restrict them from being used at a larger scale.
The purpose of the PhD project is to go one step further and address two challenges:
From the methodological point of view, we plan to explore new machine learning algorithms that tackle the problem induced by the presence of highly correlated and interdependent outcomes in multi-class classification as well as heterogeneous data.
One research axis that will be investigated is to explore the potential of deep learning to address both questions. Our objective will be to investigate novel deep architectures that will efficiently fit our needs, particularly focusing on semi-supervised networks allowing to operate on partially labeled data, which is a major characteristic of medical data.
The candidate is expected to have strong knowledge either in machine learning or image processing and a good experience in both fields. Some prior experience with medical image processing would be appreciated but is not required. Good programming skills are also required. The available code is written in Matlab and Python but other languages can be used. We are looking for an enthusiastic and autonomous student with strong motivation and interest in multidisciplinary research (image processing and machine learning in a medical context).
Interested applicants are required to send a cover letter, CV and any other relevant documents (reference letter, recent transcripts of marks,...)
 C. Lartizien, M. Rogez, E. Niaf, and F. Ricard. Computer aided staging of lymphoma patients with fdg pet/ct imaging based on textural information. IEEE Journal of Biomedical and Health informatics, 18(3):946–955, 2014.
 E. Niaf, O. Rouviere, F. Mege-Lechevallier, F. Bratan, and C. Lartizien. Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric mri. Physics in Medicine and Biology, 57(12):3833–3851, 2012.
 E. Niaf, R. Flamary, O. Rouviere, C. Lartizien, and S. Canu. Kernel-based learning from both qualitative and quantitative labels: Application to prostate cancer diagnosis based on multiparametric mr imaging. IEEE Transactions on Image Processing, 23(3):979–991, 2014.
 E. Niaf, C. Lartizien, F. Bratan, L. Roche, M. Rabilloud, F. Mège-Lechevallier, and O. Rouvière. Prostate focal peripheral zone lesions: Characterization at multiparametric mr imaging—influence of a computer-aided diagnosis system. Radiology, 271(3):761–69, 2014.
 Meriem El Azami, Alexander Hammers, Julien Jung, Nicolas Costes, Romain Bouet, and Carole Lartizien. Detection of lesions underlying intractable epilepsy on t1-weighted mri as an outlier detection problem. PloS one, 11(9):e0161498, 2016.
 M. El Azami, C. Lartizien, and S. Canu. Converting svdd scores into probability estimates: Application to outlier detection. Neurocomputing, 268:64–75, 2017.
 Z. Alaverdyan and C. Lartizien. Feature extraction with regularized siamese networks for outlier detection: application to epilepsy lesion detection. In Conférence sur l’apprentissage automatique (CAp 2017), 2017.
 J. Lehaire, R. Flamary, O. Rouviere, and C. Lartizien. Computer aided diagnostic for prostate cancer detection and characterization combining learned dictionaries and supervised classification. In IEEE international conference on image processing (ICIP), pages 2251 – 2255.
 Rahaf Aljundi, Jérôme Lehaire, Fabrice Prost-Boucle, Olivier Rouvière, and Carole Lartizien. Transfer learning for prostate cancer mapping based on multicentric MR imaging databases. In MLMMI@ICML, pages 74–82, 2015.
 L. Gautheron, I. Redko, and C. Lartizien. Adaptation de domaine pour la détection automatique du cancer de la prostate en imagerie irm multiparamétrique. In Colloque GRETSI, 2017.
(c) GdR 720 ISIS - CNRS - 2011-2018.