ArticleIS&T International Symposium on Electronic Imaging2021
Deep Learning Approach for Dynamic Sparse Sampling for High-Throughput Mass Spectrometry Imaging.
Article in IS&T International Symposium on Electronic Imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mass spectrometry-based human spatial omics: fundamentals, innovations, and applications.Journal of biomedical science · 2026Pooled it
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging (nano-DESI MSI): A Tutorial Review.ACS measurement science au · 2024Review
- [Research progress of deep learning applications in mass spectrometry imaging data analysis].Se pu = Chinese journal of chromatography · 2024Review
- High-resolution integrated microfluidic probe for mass spectrometry imaging of biological tissues.Analytica chimica acta · 2023Article
- Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy.Nature communications · 2023Article
- High-Throughput Mass Spectrometry Imaging of Biological Systems: Current Approaches and Future Directions.Trends in analytical chemistry : TRAC · 2023Article
- Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging.IEEE transactions on computational imaging · 2023Article
- Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling.ACS measurement science au · 2022Article
- Enhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling.Analytical chemistry · 2022Article
- Self-supervised clustering of mass spectrometry imaging data using contrastive learning.Chemical science · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A Supervised Learning Approach for Dynamic Sampling (SLADS) addresses traditional issues with the incorporation of stochastic processes into a compressed sensing method. Statistical features, extracted from a sample reconstruction, estimate entropy reduction with regression models, in order to dynamically determine optimal sampling locations. This work introduces an enhanced SLADS method, in the form of a Deep Learning Approach for Dynamic Sampling (DLADS), showing reductions in sample acquisition times for high-fidelity reconstructions between ~ 70-80% over traditional rectilinear scanning. These improvements are demonstrated for dimensionally asymmetric, high-resolution molecular images of mouse uterine and kidney tissues, as obtained using Nanospray Desorption ElectroSpray Ionization (nano-DESI) Mass Spectrometry Imaging (MSI). The methodology for training set creation is adjusted to mitigate stretching artifacts generated when using prior SLADS approaches. Transitioning to DLADS removes the need for feature extraction, further advanced with the employment of convolutional layers to leverage inter-pixel spatial relationships. Additionally, DLADS demonstrates effective generalization, despite dissimilar training and testing data. Overall, DLADS is shown to maximize potential experimental throughput for nano-DESI MSI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.