Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions

• URL: http://arxiv.org/abs/2211.01372v1
• Date: Wed, 2 Nov 2022 15:10:14 GMT
• Title: Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions
• Authors: Rucha Deshpande, Ashish Avachat, Frank J. Brooks, Mark A. Anastasio
• Abstract summary: We demonstrate the potential applicability of an end-to-end learning-based quantitative phase retrieval method, trained on simulated data, to experimental propagation-based x-ray phase contrast measurements.
• Score: 8.238694906165453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast imaging of heterogeneous and structurally complicated objects is challenging under laboratory conditions due to partial spatial coherence and polychromaticity. A learning-based method (LBM) provides a non-linear approach to this problem while not being constrained by restrictive assumptions about object properties and beam coherence. In this work, a LBM was assessed for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations. Towards this end, an end-to-end LBM was employed for QPR under laboratory conditions and its robustness was investigated across various system and object conditions. The robustness of the method was tested via varying propagation distances and its generalizability with respect to object structure and experimental data was also tested. Although the LBM was stable under the studied variations, its successful deployment was found to be affected by choices pertaining to data pre-processing, network training considerations and system modeling. To our knowledge, we demonstrated for the first time, the potential applicability of an end-to-end learning-based quantitative phase retrieval method, trained on simulated data, to experimental propagation-based x-ray phase contrast measurements acquired under laboratory conditions. We considered conditions of polychromaticity, partial spatial coherence, and high noise levels, typical to laboratory conditions. This work further explored the robustness of this method to practical variations in propagation distances and object structure with the goal of assessing its potential for experimental use. Such an exploration of any LBM (irrespective of its network architecture) before practical deployment provides an understanding of its potential behavior under experimental settings.

Related papers

• Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments [67.80453452949303]
  Estimating the conditional average treatment effect (CATE) from observational data is relevant for many applications such as personalized medicine. Here, we focus on the widespread setting where the observational data come from multiple environments. We propose different model-agnostic learners (so-called meta-learners) to estimate the bounds that can be used in combination with arbitrary machine learning models.
  arXiv  Detail & Related papers  (2024-06-04T16:31:43Z)
• The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective [0.32634122554913997]
  Large Language Models (LLMs) have demonstrated impressive potential to simulate human behavior. We show that variations in the treatment included in the prompt can cause variations in unspecified confounding factors. We propose a theoretical framework suggesting this endogeneity issue generalizes to other contexts.
  arXiv  Detail & Related papers  (2023-12-24T16:32:35Z)
• Conditional Kernel Imitation Learning for Continuous State Environments [9.750698192309978]
  We introduce a novel conditional kernel density estimation-based imitation learning framework. We show consistently superior empirical performance over many state-of-the-art IL algorithms.
  arXiv  Detail & Related papers  (2023-08-24T05:26:42Z)
• Causal Inference under Data Restrictions [0.0]
  This dissertation focuses on modern causal inference under uncertainty and data restrictions. It includes applications to neoadjuvant clinical trials, distributed data networks, and robust individualized decision making.
  arXiv  Detail & Related papers  (2023-01-20T20:14:32Z)
• Robustness in Fatigue Strength Estimation [61.85933973929947]
  In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments.
  arXiv  Detail & Related papers  (2022-12-02T12:30:29Z)
• Provable RL with Exogenous Distractors via Multistep Inverse Dynamics [85.52408288789164]
  Real-world applications of reinforcement learning (RL) require the agent to deal with high-dimensional observations such as those generated from a megapixel camera. Prior work has addressed such problems with representation learning, through which the agent can provably extract endogenous, latent state information from raw observations. However, such approaches can fail in the presence of temporally correlated noise in the observations.
  arXiv  Detail & Related papers  (2021-10-17T15:21:27Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Experimental certification of nonclassicality via phase-space inequalities [58.720142291102135]
  We present the first experimental implementation of the recently introduced phase-space inequalities for nonclassicality certification. We demonstrate the practicality and sensitivity of this approach by studying nonclassicality of a family of noisy and lossy quantum states of light.
  arXiv  Detail & Related papers  (2020-10-01T09:03:52Z)
• Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
  Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
  arXiv  Detail & Related papers  (2020-07-31T17:25:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.