Isabelle Bloch

How is Artificial Intelligence Changing Science?

Research in the Era of Learning Algorithms

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Prof. Isabelle Bloch

Cooperation Partner

Artificial Intelligence Chair
Sorbonne University

Sorbonne Université
Boîte courrier 169
Couloir 26-00, Étage 5, Bureau 511
4 place Jussieu
75252 Paris

Tel: +33(0) 1 44 27 87 33

Education & Research Experience

  • 1986: graduated, Ecole des Mines de Paris
  • 1987: M.A., University Paris 12
  • 1990: Ph.D. Ecole Nationale Supérieure des Télécommunications (Télécom Paris)
  • 1995: Habilitation degree, University Paris 5
  • until 2020: Professor at Télécom Paris
  • 2020 – present: Professor at Sorbonne Université

Relevant Publications

  • Bloch, I., Clouard, R., Revenu, M., & Sigaud, O. (2019). Artifical Intelligence and Pattern Recognition, Vision, Learning. In P. Marquis, O. Papini, H. Prade (Eds.), A Guided Tour of Artificial Intelligence Research, Springer.
  • Bloch, I. (2018). Modèles symboliques pour la reconnaissance de structures dans les images médicales. In S. Ranwez, G. Dray (Eds.), IDs’16 Extraction, Modélisation, Gestion de Connaissances, (pp. 39-48). Presses des Mines.
  • Xu, Y., Géraud, T., Puybareau, É., Bloch, I., Chazalon, J. (2018). White Matter Hyperintensities Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning. In Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes. Lecture Notes in Computer Science, 10670. Springer,
  • Muller, C., Mille, E., Virzi, A., Marret, J.-B., Peyrot, Q., Delmonte, A., Berteloot, L., Gori, P., Blanc, Th., Grevent, D., Boddaert, N., Bloch, I., & Sarnacki, S. (2019). Integrating tractography in pelvic surgery: a proof of concept, Journal of Pediatric Surgery Case Reports, 48.
  • Couteaux, V., Si-Mohamed, S., Renard-Penna, R., Nempont, O., Lefevre, T., Popoff, A., Pizaine, G., Villain, N., Bloch, I., Behr, J., Bellin, M.-F., Roy, C., Rouvière, O., Montagne, S., Lassau N., & Boussel, L. (2019). Kindey Cortex Segmentation in 2D CT with U-Nets Ensemble Aggregation, Diagnostic and Interventional Imaging, 100 (pp. 211-217).
  • Couteaux, V., Si-Mohamed, S., Nempont, O., Lefevre, T., Popoff, A., Pizaine, G., Villain, N., Bloch, I., Cotten A., & Boussel, L. (2019). Automatic Knww Meniscus Tear Detection and Orientation Classification with Mask-RCNN, Diagnostic and Interventional Imaging, 100 (pp. 235-242).
  • Virzi, A., Muller, C., Marret, J.-B., Mille, E., Berteloot, L., Grevent, D., Boddaert, N., Gori, P., Sarnacki, S., & Bloch, I. (2019). Comprehensive review of 3D segmentation software tools for MRI usable for pelvic surgery planning, Journal of Digital Imaging.
  • Aiguier, M., & Bloch, I. (2019). Logical Dual Concepts based on Mathematical Morphology in Stratified Institutions, Journal of Applied Non-Classifcal Logics.
  • Abou-Elailah, A.-B., Bloch, I., & Gouet-Brunet, V. (2018). Unsupervised detection of ruptures in spatial relationships in video sequences based on log-likelihood ratio, Pattern Analysis and Applications, 21, (pp. 829-846).
  • Aiguier, M., Atif, J., Bloch, I., & Hudelot, C. (2018). Belief Revision, Minimal Change and Relaxation: A General Framework based on Satisfaction Systems, and Applications to Description Logics, Artificial Intelligence, 256, (pp. 160-180).
  • Xu, Y., Morel, B., Dahdouh, S., Puybareau, E., Virzi, A., Urien, H., Géraud, Th., Adamsbaum, C., & Bloch, I. (2018). The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities, Medical Image Analysis, 48, (pp. 75-94).
  • Aiguier, M., Atif, J., Bloch, I., & Pino Pérez, R. (2018). Explanatory relations in arbitrary logics based on satisfaction systems, cutting and retraction, International Journal of Approximate Reasoning, 102, (pp. 1-20).
  • Commowick, O., Istace, A., Kain, M., Laurent, B., Leray, F., Simon, M., Ameli, R., Ferre, J.C., Tourdias, T., Cervenansky, F., Glatard, T., Beaumont, J., Senan, D., Forbes, F., Knight, J., Khademi, A., Mahbod, A., Wang, Ch., McKinley, R., Wagner, F., Muschelli, J., Sweeney, E., Roura, E., Llado, X., Santos, M.M., Santos, W.P., Silva-Filho, A.G., Tomas-Fernandez, X., Urien, H., Bloch, I., Valverde, S., Cabezas, M., Vera-Olmos, F.J., Malpica, N., Guttmann, C., Vukusic, S., Edan, G., Dojat, M., Styner, M., Warfield, S.K., Cotton, F., & Barillot, C. (2018). Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure, Nature Scientific Reports, 8(13650), (pp. 1-17).
  • Tor-Diez, C., Passat, N., Bloch, I., Faisan, S., Bednarek, N., & Rousseau, F. (2018). An Iterative Multi-Atlas Patch-Based Approach for Cortex Segmentation from Neonatal MRI, Computerized Medical Imaging and Graphics, 70, (pp. 73-82).