Free annotated data for deep learning in microscopy? A hitchhiker's guide
This work provides a guide for researchers in bio-microscopy to reduce data annotation burdens, though it is incremental as it reviews existing methods.
The paper addresses the challenge of high annotation costs in microscopy by exploring methods to train deep learning models using knowledge from other fields, aiming to make these techniques more practical.
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.