ArticleIEEE signal processing magazine2017
Optimal Mass Transport: Signal processing and machine-learning applications.
Article in IEEE signal processing magazine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analyses of mouse and human prostate single-cell transcriptomes reveal widespread epithelial plasticity in tissue regression, regeneration, and cancer.Genome medicine · 2025Pooled it
- STREAM: A data-driven framework for physiological state monitoring in ICU patients.PLOS digital health · 2026Article
- Fast Rigid Alignment of Heterogeneous Images in Sliced Wasserstein Distance.SIAM journal on imaging sciences · 2026Article
- Profiling adaptive immunity: A quantitative framework for immune repertoire dynamics and clinical diagnostics.Fundamental research · 2026Article
- Article
- Distribution-based covariate assessment using wasserstein distance in population pharmacokinetic models.Frontiers in pharmacology · 2026Article
- Machine learning for N-dimensional spatial reasoning tasks on the web.Frontiers in bioinformatics · 2026Article
- A sub-Riemannian model of the motor cortex with Wasserstein distance.Frontiers in computational neuroscience · 2026Article
- Article
- Predictive modeling of hematoma expansion from non-contrast computed tomography in spontaneous intracerebral hemorrhage patients.medRxiv : the preprint server for health sciences · 2025Article
- Dynamic gene regulatory network inference from single-cell data using optimal transport.Bioinformatics (Oxford, England) · 2025Article
- Linear Optimal Transport Subspaces for Point Set Classification.Journal of mathematical imaging and vision · 2025Article
- Partial differential equations in data science.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Local Sliced Wasserstein Feature Sets for Illumination Invariant Face Recognition.Pattern recognition · 2025Article
- Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data.... International Conference on Learning Representations · 2025Article
- A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity.Scientific reports · 2025Article
- Low responsiveness of machine learning models to critical or deteriorating health conditions.Communications medicine · 2025Article
- Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025Article
- Biological correlates associated with high-risk breast cancer patients identified using a computational method.NPJ breast cancer · 2025Article
- Maximum-entropy and subspace methods for high-resolution relaxation-diffusion distribution estimation.Imaging neuroscience (Cambridge, Mass.) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Transport-based techniques for signal and data analysis have received increased attention recently. Given their ability to provide accurate generative models for signal intensities and other data distributions, they have been used in a variety of applications including content-based retrieval, cancer detection, image super-resolution, and statistical machine learning, to name a few, and shown to produce state of the art results in several applications. Moreover, the geometric characteristics of transport-related metrics have inspired new kinds of algorithms for interpreting the meaning of data distributions. Here we provide a practical overview of the mathematical underpinnings of mass transport-related methods, including numerical implementation, as well as a review, with demonstrations, of several applications. Software accompanying this tutorial is available at [43].
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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.