Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs.

Thiago L T da Silveira, Paulo G L Pinto, Thiago S Lermen, Cláudio R Jung

Open access · greenAbstract read

In one paragraph

Article in Journal of visual communication and image representation, 2023. 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

0.2field-weighted citation impact, top 46% of its field

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, 1 citations in OpenAlex.

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

4 authors at 1 institution in 1 country.

Thiago L T da SilveiraInstitute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil.

Paulo G L PintoInstitute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil.

Thiago S LermenInstitute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil.

Cláudio R JungInstitute of Informatics - Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil.

Universidade Federal do Rio Grande do Sul · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Coronavirus Disease 2019 (COVID-19) has drastically overwhelmed most countries in the last two years, and image-based approaches using computerized tomography (CT) have been used to identify pulmonary infections. Recent methods based on deep learning either require time-consuming per-slice annotations (2D) or are highly data- and hardware-demanding (3D). This work proposes a novel omnidirectional 2.5D representation of volumetric chest CTs that allows exploring efficient 2D deep learning architectures while requiring volume-level annotations only. Our learning approach uses a siamese feature extraction backbone applied to each lung. It combines these features into a classification head that explores a novel combination of Squeeze-and-Excite strategies with Class Activation Maps. We experimented with public and in-house datasets and compared our results with state-of-the-art techniques. Our analyses show that our method provides better or comparable prediction quality and accurately distinguishes COVID-19 infections from other kinds of pneumonia and healthy lungs.

Indexed as

2.5D representationCOVID-19 diagnosisGround-glass opacityOmnidirectional imaging

Identifiers

PMID36741546

DOI10.1016/j.jvcir.2023.103775

PMCPMC9886432

OpenAlexW4319294815

What OpenQuestion holds

Textmetadata

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.