Post-doc - Deep learning for multimodal imaging and oncology applications

The post-doc will be in charge of investigating the use of machine learning methods and more specifically deep learning and transfer learning approaches to address the various challenges in the development of multiparametric models in oncology.
1. Characterization of PET/CT images using texture analysis: the past, the present… any future? Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Eur J Nucl Med Mol Imaging. 2016 Jun 6. [Epub ahead of print]
2. Comparison of Tumor Uptake Heterogeneity Characterization Between Static and Parametric 18F-FDG PET Images in Non-Small Cell Lung Cancer. Tixier F, Vriens D, Cheze-Le Rest C, Hatt M, Disselhorst JA, Oyen WJ, de Geus-Oei LF, Visser EP, Visvikis D. J Nucl Med. 2016 Jul;57(7):1033-9
3. Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Desseroit MC, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, Cheze Le Rest C, Hatt M. Eur J Nucl Med Mol Imaging. 2016 Jul;43(8):1477-85
4. SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET.
Lapuyade-Lahorgue J, Visvikis D, Pradier O, Cheze Le Rest C, Hatt M. Med Phys. 2015 Oct;42(10):5720-34
5. Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer?
Groheux D, Majdoub M, Tixier F, Le Rest CC, Martineau A, Merlet P, Espié M, de Roquancourt A, Hindié E, Hatt M, Visvikis D. Eur J Nucl Med Mol Imaging. 2015 Oct;42(11):1682-91
6. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. Hatt M, Majdoub M, Vallières M, Tixier F, Le Rest CC, Groheux D, Hindié E, Martineau A, Pradier O, Hustinx R, Perdrisot R, Guillevin R, El Naqa I, Visvikis D. J Nucl Med. 2015 Jan;56(1):38-44

Keywords: Radiomics - medical imaging - oncology - deep learning - machine learning - texture analysis. - Our group investigates the design and validation of predictive models exploiting multi-modal imaging (radiomics) and other –omics data for diagnosis and therapy response evaluation in oncology - and radiotherapy planning. These investigations are associated with numerous challenges to address and pitfalls to avoid. Recently deep learning techniques have emerged as promising tools in several fields - including medical image segmentation and radiomics.