Deep learning: A framework for image analysis in life sciences

Scientists are constantly seeking imaging systems that are faster, more powerful and capable of supporting longer observation times. This is especially true in life sciences, where objects of interest are rarely visible to the naked eye. As technological progress allows us to study life on ever smaller scales of time and space, often at less than nanoscale, researchers are also turning to increasingly powerful artificial intelligence programs to sort through and analyze these vast datasets. Deep learning models—a type of machine learning algorithm that uses multi-layer networks to extract insights from raw input—are growing in popularity among life sciences researchers on account of their speed and precision. Yet using these models without fully understanding their architecture and their limitations introduces the risk of bias and error, with potentially major consequences. Scientists from the EPFL Center for Imaging and EMBL-EBI (Cambridge, UK) explore these challenges one by one in a paper published in IEEE Signal Processing Magazine. The team outlines good practices for employing deep learning technologies in life sciences and advocates for closer interdisciplinary collaboration between bioscience researchers and program developers.