Challenges for machine learning in clinical translation of big data imaging studies
It addresses challenges for applying big data imaging in clinical settings, but is incremental as it reviews existing issues rather than proposing new solutions.
The paper examines barriers to clinical translation of deep learning in neuroimaging, focusing on data availability, interpretability, and evaluation, without presenting specific results or numbers.
The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology. However, despite the success of deep learning when applied to many neuroimaging tasks, there remain barriers to the clinical translation of large-scale datasets and processing tools. Here, we explore the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation and logistical challenges, and discuss the challenges we believe are still to be overcome to enable the full success of big data deep learning approaches to be experienced outside of the research field.