Taman Upadhaya

I am a Postdoctoral researcher at University hospital of Poitiers, Poitiers, France. My research is focused on prognostic/predictive models for oncology using PET/CT/MRI-based "deep learning", "radiomics" and "machine learning" algorithms..

General research interests

Medical imaging and human brain, image processing, machine learning and pattern recognition, prognostic predictive models, tracking, radiomics and radiogenomics.

Publications

2018

  • Shima Sepehri, Taman Upadhaya, Marie-Charlotte Desseroit, Dimitris Visvikis, Catherine Cheze Le Rest, and Mathieu Hatt. Comparision of machine learning algorithm for building prognostic models in non-small cell lung cancer using clinical and radiomics features from 18f-fdg pet/ct images. In Society of Nuclear Medicine and Molecular Imaging (SNMMI) annual meeting. 2018.
    [BibTeX▼]
  • Taman Upadhaya, Minea Hadzic, Floriane Legot, Mathieu Hatt, Dimitris Visvikis, and Catherine Cheze-Le Rest. Radiomics based deep fully connected neural network (r-dnn) for prognostication of lung cancer. In Society of Nuclear Medicine and Molecular Imaging (SNMMI) annual meeting. 2018.
    [BibTeX▼]
  • Shima Sepehri, Taman Upadhaya, Marie-Charlotte Desseroit, Dimitris Visvikis, Catherine Cheze Le Rest, and Mathieu Hatt. Comparison of machine learning algorithms, and pre-processing, for building prognostic models in non-small cell lung cancer using clinical and radiomics features from 18f-fdg pet/ct images. In Annual congress of the European Association of Nuclear Medicine. 2018.
    [BibTeX▼]

2017

  • S. Sanduleanu, Taman Upadhaya, A.J.G. Even, A. Jochems, R.T.H. Leijenaar, F. Dankers, M. Hatt, J.H.A.M. Kaanders, and P. Lambin. Non-invasive imaging for tumor hypoxia: a novel externally validated ct-based radiomics signature. In The 15th Acta Oncologica conference on biology-guided adaptive radiotherapy​. 2017.
    [BibTeX▼]
  • Taman Upadhaya, G. Disseaux, Le Reste P-J., U. Schick, and M. Hatt. Multimodal mri-derived radiomics for gbm prognostic model: comparison of machine learning algorithms for overall and progression free survival. In 2 ème congrès national d’imagerie du vivant​. 2017.
    [BibTeX▼]
  • Taman Upadhaya, Pierre-Jean Le Reste, Mathieu Hatt, and Catherine Cheze-Le Rest. Gbm and lung cancer prognostic model: comparison of machine learning algorithms for overall survival. In 13 ème Journées du Cancéropôle Grand Sud-Ouest​. 2017.
    [BibTeX▼]
  • Taman Upadhaya and et al. Image biomarker standardisation initiative (ibsi), multicenter initative for standardisation of image biomarkers. In European SocieTy for Radiotherapy & Oncology annual meeting, IBSI. 2017.
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2016

  • Taman Upadhaya, Yannick Morvan, Eric Stindel, Le Reste, Mathieu Hatt, and others. Prognosis classification in glioblastoma multiforme using multimodal mri derived heterogeneity textural features: impact of pre-processing choices. In SPIE Medical Imaging​. 2016.
    [BibTeX▼]
  • Taman Upadhaya, Yannick Morvan, Eric Stindel, Le Reste, Mathieu Hatt, and others. Multimodal mri radiomics in gbm: a comparative investigation of feature selection and classification techniques for prognostic models including robustness assessment. In IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)​. 2016.
    [BibTeX▼]

2015

  • Taman Upadhaya, Yannick Morvan, Eric Stindel, Le Reste, Mathieu Hatt, and others. Prognostic value of multimodal mri tumor features in glioblastoma multiforme using textural features analysis. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 50–54. IEEE, 2015.
    [BibTeX▼]
  • Taman Upadhaya, Yannick Morvan, Eric Stindel, Le Reste, Mathieu Hatt, and others. A framework for multimodal imaging-based prognostic model building: application to multimodal mri in glioblastoma multiforme. In Recherche en Imagerie et Technologies pour la Santé (RITS). 2015.
    [BibTeX▼]
  • Taman Upadhaya, Y Morvan, E Stindel, P-J Le Reste, and M Hatt. A framework for multimodal imaging-based prognostic model building: preliminary study on multimodal mri in glioblastoma multiforme. IRBM, 36(6):345–350, 2015.
    [BibTeX▼]