Free annotated data for deep learning in microscopy? A hitchhiker’s guide

¹ Idiap Research Institute, Rue Marconi 19, 1920 Martigny, Switzerland
² École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015 Lausanne, Switzerland
³ University of California, Santa Barbara, CA 93106-9010, USA

^* e-mail : michael.liebling@idiap.ch

Résumé

In microscopy, the time burden and cost of acquiring and annotating large datasets that many deep learning models take as a prerequisite, often appears to make these methods impractical. Can this requirement for annotated data be relaxed? Is it possible to borrow the knowledge gathered from datasets in other application fields and leverage it for microscopy? Here, we aim to provide an overview of methods that have recently emerged to successfully train learning-based methods in bio-microscopy.