Evidence map›Paper›PMID 26858466›Full record

ArticlePattern recognition2016

A continuous linear optimal transport approach for pattern analysis in image datasets.

Soheil Kolouri, Akif B Tosun, John A Ozolek, Gustavo K Rohde

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Pattern recognition, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  7. Article
  8. Article
  9. Article
  10. A TransportationJournal of mathematical imaging and vision · 2017
    Article
  11. Article
  12. Article
  13. The Radon Cumulative Distribution Transform and Its Application to Image Classification.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2016
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Soheil KolouriBiomedical Engineering Department, Carnegie Mellon University, C120 Hamerschlag Hall, Pittsburgh, PA 15213, USA.
Akif B TosunBiomedical Engineering Department, Carnegie Mellon University, C120 Hamerschlag Hall, Pittsburgh, PA 15213, USA.
John A OzolekBiomedical Engineering Department, Carnegie Mellon University, C120 Hamerschlag Hall, Pittsburgh, PA 15213, USA; Department of Pathology, Children's Hospital of Pittsburgh, Pittsburgh, PA 15224, USA.
Gustavo K RohdeBiomedical Engineering Department, Carnegie Mellon University, C120 Hamerschlag Hall, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Funding

TrainingP41GM103712 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FAEDER, JAMES · 2012 to 2021
$16.3M
Image-derived Spatiotemporal Models of Cellular Organization and PerturbationR01GM090033 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI MURPHY, ROBERT F, ROHDE, GUSTAVO KUNDE · 2010 to 2018
$2.4M
Utility of Effusion Cytology and Image Analysis in the Diagnosis of MesotheliomaR21CA188938 · NCI · UNIVERSITY OF VIRGINIA · PI ROHDE, GUSTAVO KUNDE · 2014 to 2015
$193k
NCI NIH HHS R21 CA188938NIGMS NIH HHS P41 GM103712NIGMS NIH HHS R01 GM090033
6 · The paper itself

Abstract

We present a new approach to facilitate the application of the optimal transport metric to pattern recognition on image databases. The method is based on a linearized version of the optimal transport metric, which provides a linear embedding for the images. Hence, it enables shape and appearance modeling using linear geometric analysis techniques in the embedded space. In contrast to previous work, we use Monge's formulation of the optimal transport problem, which allows for reasonably fast computation of the linearized optimal transport embedding for large images. We demonstrate the application of the method to recover and visualize meaningful variations in a supervised-learning setting on several image datasets, including chromatin distribution in the nuclei of cells, galaxy morphologies, facial expressions, and bird species identification. We show that the new approach allows for high-resolution construction of modes of variations and discrimination and can enhance classification accuracy in a variety of image discrimination problems.

Indexed as

Generative image modelingLinear embeddingOptimal transportPattern visualization

Identifiers

PMID26858466
PMCPMC4742369

What OpenQuestion holds

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Registered trials

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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.