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

arXiv: https://arxiv.org/abs/2210.06891 · HTML: https://mlsanity.com/paper/2210.06891/

## 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.

## Builds on (most cited first, 28 of 28)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design](https://mlsanity.com/paper/2103.02438/) (2021)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [Partial Counterfactual Identification from Observational and Experimental Data](https://mlsanity.com/paper/2110.05690/) (2021)
- [Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods](https://mlsanity.com/paper/2111.02329/) (2021)
- [Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge](https://mlsanity.com/paper/2001.02518/) (2020)
- [Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation](https://mlsanity.com/paper/2002.08129/) (2020)
- [High-Dimensional Experimental Design and Kernel Bandits](https://mlsanity.com/paper/2105.05806/) (2021)
- [Zero-Shot Self-Supervised Learning for MRI Reconstruction](https://mlsanity.com/paper/2102.07737/) (2021)
- [Interventions, Where and How? Experimental Design for Causal Models at Scale](https://mlsanity.com/paper/2203.02016/) (2022)
- [Optimizing Sequential Experimental Design with Deep Reinforcement Learning](https://mlsanity.com/paper/2202.00821/) (2022)
- [BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design](https://mlsanity.com/paper/1909.04568/) (2019)
- [Feature Importance Ranking for Deep Learning](https://mlsanity.com/paper/2010.08973/) (2020)
- [HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction](https://mlsanity.com/paper/2203.08213/) (2022)
- [Probabilistic Active Meta-Learning](https://mlsanity.com/paper/2007.08949/) (2020)
- [Kernel Feature Selection via Conditional Covariance Minimization](https://mlsanity.com/paper/1707.01164/) (2017)
- [GeneDisco: A Benchmark for Experimental Design in Drug Discovery](https://mlsanity.com/paper/2110.11875/) (2021)
- [An Experimental Design Perspective on Model-Based Reinforcement Learning](https://mlsanity.com/paper/2112.05244/) (2021)
- [Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach](https://mlsanity.com/paper/2006.05591/) (2020)
- [A/B Testing for Recommender Systems in a Two-sided Marketplace](https://mlsanity.com/paper/2106.00762/) (2021)
- [LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments](https://mlsanity.com/paper/2006.09670/) (2020)
- [Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces](https://mlsanity.com/paper/2205.13627/) (2022)
- [Normalizing Flows for Knockoff-free Controlled Feature Selection](https://mlsanity.com/paper/2106.01528/) (2021)
- [Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale](https://mlsanity.com/paper/2008.12025/) (2020)
- [Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls](https://mlsanity.com/paper/2112.00278/) (2021)
- [Online Balanced Experimental Design](https://mlsanity.com/paper/2203.02025/) (2022)
- [Progressive Subsampling for Oversampled Data - Application to Quantitative MRI](https://mlsanity.com/paper/2203.09268/) (2022)

## 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