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

TL;DR

This paper introduces TADRED, a task-driven experimental design method that optimizes image channel selection and model training simultaneously, reducing acquisition time and costs across various imaging applications.

Contribution

The paper proposes a novel paradigm and method, TADRED, for selecting informative image channels tailored to specific tasks while training models, improving over classical and recent feature selection approaches.

Findings

TADRED outperforms classical experimental design methods.

It achieves better results than recent application-specific methods.

State-of-the-art performance in supervised feature selection across diverse imaging tasks.

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