Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection

• URL: http://arxiv.org/abs/2210.06891v4
• Date: Sun, 17 Mar 2024 11:45:52 GMT
• Title: Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection
• Authors: Stefano B. Blumberg, Paddy J. Slator, Daniel C. Alexander,
• Abstract summary: We present a new paradigm for experimental design that simultaneously optimize the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task. 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.
• Score: 2.662628670752034
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

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