  This paper presents a data-driven, task-specific paradigm for experimental
design, to shorten acquisition time, reduce costs, and accelerate the
deployment of imaging devices. Current approaches in experimental design focus
on model-parameter estimation and require specification of a particular model,
whereas in imaging, other tasks may drive the design. Furthermore, such
approaches often lead to intractable optimization problems in real-world
imaging applications. Here we present a new paradigm for experimental design
that simultaneously optimizes the design (set of image channels) and trains a
machine-learning model to execute a user-specified image-analysis task. The
approach obtains data densely-sampled over the measurement space (many image
channels) for a small number of acquisitions, then identifies a subset of
channels of prespecified size that best supports the task. We propose a method:
TADRED for TAsk-DRiven Experimental Design in imaging, to identify the most
informative channel-subset whilst simultaneously training a network to execute
the task given the subset. Experiments demonstrate the potential of TADRED in
diverse imaging applications: several clinically-relevant tasks in magnetic
resonance imaging; and remote sensing and physiological applications of
hyperspectral imaging. Results show substantial improvement over classical
experimental design, two recent application-specific methods within the new
paradigm, and state-of-the-art approaches in supervised feature selection. We
anticipate further applications of our approach. Code is available:
https://github.com/sbb-gh/experimental-design-multichannel
