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 standard 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 are often lead to intractable optimisation problems in real-world imaging applications. Here we put forward 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 pre-specified 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 we explore, and state-of-the-art approaches in supervised feature selection.  We anticipate further applications of our approach; code (for reviewers) is available: \cite{ouranonymouscode}.
Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection
Stefano B. Blumberg Paddy J. Slator Daniel C. Alexander