mlsanityData to 14 Oct 2026

Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior

arXiv 2207.05714 ↗First posted 11 Jul 2022cs.CV cs.LGcited by 7

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Abstract (arXiv)

We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction. We propose a novel approach using the linearised deep image prior. It allows incorporating information from the pilot measurements into the angle selection criteria, while maintaining the tractability of a conjugate Gaussian-linear model. On a synthetically generated dataset with preferential directions, linearised DIP design allows reducing the number of scans by up to 30% relative to an equidistant angle baseline.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 11 Jul 2022.

Builds on

14 of 35 references.

References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017Deep Image Priorcited by 1,098
  3. 2020Finite Versus Infinite Neural Networks: an Empirical Studycited by 157
  4. 2021Scalable Marginal Likelihood Estimation for Model Selection in Deep Learningcited by 106
  5. 2020Getting a CLUE: A Method for Explaining Uncertainty Estimatescited by 92
  6. 2020Bayesian Deep Learning via Subnetwork Inferencecited by 87
  7. 2020Pathwise Conditioning of Gaussian Processescited by 64
  8. 2020Depth Uncertainty in Neural Networkscited by 57
  9. 2020Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methodscited by 56
  10. 2022Adapting the Linearised Laplace Model Evidence for Modern Deep Learningcited by 29
  11. 2020Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imagingcited by 13
  12. 2019Disentangling and Learning Robust Representations with Natural Clusteringcited by 10
2 more
  1. 2021Edge-promoting adaptive Bayesian experimental design for X-ray imagingcited by 10
  2. 2020Sequentially optimized projections in X-ray imagingcited by 6

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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 0; 2025: 2.

7 papers.

  1. 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient Descentcited by 27
  2. 2022Sampling-based inference for large linear models, with application to linearised Laplacecited by 18
  3. 2023Promises and Pitfalls of the Linearized Laplace in Bayesian Optimizationcited by 10
  4. 2023Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learningcited by 5
  5. 2025Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomographycited by 4
  6. 2023Image Reconstruction via Deep Image Prior Subspacescited by 1
  7. 2025Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stoppingcited by 1