Evidence map›Paper›PMID 35831385›Full record

ArticleScientific reports2022

A semi-automatic toolbox for markerless effective semantic feature extraction.

Vito Paolo Pastore, Matteo Moro, Francesca Odone

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Vito Paolo PastoreItalian Institute of Technology (IIT), Genova, Italy. Vito.Paolo.Pastore@unige.it.
Matteo MoroDepartment of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova and with the Machine Learning Genoa (MaLGa) Center, Genova, Italy.
Francesca OdoneDepartment of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova and with the Machine Learning Genoa (MaLGa) Center, Genova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

VisionTool is an open-source python toolbox for semantic features extraction, capable to provide accurate features detectors for different applications, including motion analysis, markerless pose estimation, face recognition and biological cell tracking. VisionTool leverages transfer-learning with a large variety of deep neural networks allowing high-accuracy features detection with few training data. The toolbox offers a friendly graphical user interface, efficiently guiding the user through the entire process of features extraction. To facilitate broad usage and scientific community contribution, the code and a user guide are available at https://github.com/Malga-Vision/VisionTool.git .

Indexed as

SemanticsSoftwareCell Tracking

Identifiers

PMID35831385
PMCPMC9279291

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.