One of the major technological success stories of the last decade has been the advent
of deep learning (DL), which has touched almost every aspect of modern life after a
breakthrough performance in an image detection challenge in 2012. The bioimaging
community quickly recognised the prospect of the automated ability to make sense of
image data with near-human performance as potentially ground-breaking. In the
decade since, hundreds of publications have used this technology to tackle many
problems related to image analysis, such as labelling or counting cells, identifying
cells or organelles of interest in large image datasets, or removing noise or improving
the resolution of images. However, the adoption of DL tools in large parts of the
bioimaging community has been slow, and many tools have remained in the hands of
developers. In this project, I have identified key barriers which have prevented many
bioimage analysts and microscopists from accessing existing DL technology in their
field and have, in collaboration with colleagues, developed the ZeroCostDL4Mic
platform, which aims to address these barriers. This project is inspired by the
observation that the most significant impact technology can have in science is when it
becomes ubiquitous, that is, when its use becomes essential to address the
community’s questions. This work represents one of the first attempts to make DL
tools accessible in a transparent, code-free, and affordable manner for bioimage
analysis to unlock the full potential of DL via its democratisation for the bioimaging
community