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

arXiv 2210.06891 ↗First posted 13 Oct 2022cs.LG cs.AI q-bio.NCcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 109 references that are in the map. First posted on arXiv 13 Oct 2022.

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28 of 109 references.

References · 28

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16 more
  1. 2019BINOCULARS for Efficient, Nonmyopic Sequential Experimental Designcited by 35
  2. 2020Feature Importance Ranking for Deep Learningcited by 31
  3. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
  4. 2020Probabilistic Active Meta-Learningcited by 23
  5. 2017Kernel Feature Selection via Conditional Covariance Minimizationcited by 20
  6. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  7. 2021An Experimental Design Perspective on Model-Based Reinforcement Learningcited by 16
  8. 2020Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approachcited by 15
  9. 2021A/B Testing for Recommender Systems in a Two-sided Marketplacecited by 11
  10. 2020LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experimentscited by 9
  11. 2022Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spacescited by 9
  12. 2021Normalizing Flows for Knockoff-free Controlled Feature Selectioncited by 5
  13. 2020Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Talecited by 3
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  15. 2022Online Balanced Experimental Designcited by 1
  16. 2022Progressive Subsampling for Oversampled Data - Application to Quantitative MRIcited by 1

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