David Rousseau

David Rousseau

Université d’Angers

David Rousseau received the M.S. degree in physics and signal processing from the Institut de Recherche Coordination Acoustique et Musique (IRCAM), Paris, France, in 1996 and the Ph.D. degree in signal and image processing from Université d’Angers, Angers, France, in 2004. From 2010 to 2017, he was a Full Professor of image processing applied to bioimaging with CREATIS, Université Lyon 1, France. Since 2018, he heads a Bioimaging Research Group with Université d’Angers. His research interests currently include computational instrumentation, machine learning based computer vision, and their applications to life sciences. Contact: [email protected]

Digital twins for virtual instrumentation and machine learning; application in life sciences

In this talk I will illustrate how the concept of digital twins can be used to reduce the cost of instrumentation design or the labelling effort in supervised machine learning. This will be illustrated with various recent bioimaging use cases [1-5] developed in my group ImHorPhen at Université d’Angers, France ([1] Douarre, C., Crispim-Junior, C. F., Gelibert, A., Germain, G., Tougne, L., & Rousseau, D. (2021). CTIS-Net: a neural network architecture for compressed learning based on Computed Tomography Imaging Spectrometers. IEEE Transactions on Computational Imaging. [2] Turgut, K., Dutagaci, H., Galopin, G., & Rousseau, D. (2020). Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods. arXiv preprint arXiv:2012.11489. [3] Ahmad, A., Frindel, C., & Rousseau, D. (2020). Detecting differences of fluorescent markers distribution in single cell microscopy: textural or pointillist feature space?. Frontiers in Robotics and AI, 7, 39. [4] Debs, N., Rasti, P., Victor, L., Cho, T. H., Frindel, C., & Rousseau, D. (2020). Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Computers in biology and medicine, 116, 103579.