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 (https://www.youtube.com/channel/UCsd9Dt6N7O-fydynsWEfkww).[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.