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

翻译：本文提出一种数据驱动、任务特定的实验设计范式，旨在缩短采集时间、降低成本并加速成像设备的部署。当前实验设计方法侧重于模型参数估计，需要指定特定模型，然而在成像领域，其他任务可能主导设计目标。此外，此类方法在实际成像应用中常导致难以处理的优化问题。本文提出一种新的实验设计范式，可同时优化设计方案（图像通道集）并训练机器学习模型执行用户指定的图像分析任务。该方法通过少量采集获取测量空间中的密集采样数据（多图像通道），继而从预设规模的候选通道中识别出最优支撑任务的子集。我们提出TADRED（成像任务驱动实验设计）方法，在识别最具信息量的通道子集的同时，训练神经网络基于该子集执行目标任务。实验展示了TADRED在多种成像场景中的潜力：包括磁共振成像的若干临床相关任务，以及高光谱成像的遥感与生理应用领域。结果表明，该方法相较于经典实验设计、新范式下的两种近期专用方法以及监督特征选择领域的最新方法均有显著提升。我们期待该方法在更广范围的应用。代码获取地址：https://github.com/sbb-gh/experimental-design-multichannel