Dealing with distribution mismatch in semi-supervised deep learning for COVID-19 detection using chest X-ray images: A novel approach using feature densities.

Saul Calderon-Ramirez, Shengxiang Yang, David Elizondo, Armaghan Moemeni

Open access · greenAbstract read

In one paragraph

Article in Applied soft computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

3.3field-weighted citation impact, top 8% 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

1 citing paper in PubMed, 23 citations in OpenAlex.

Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023
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 at 3 institutions in 2 countries.

Saul Calderon-RamirezInstitute of Artificial Intelligence (IAI), School of Computer Science and Informatics, De Montfort University, United Kingdom.

Shengxiang YangInstitute of Artificial Intelligence (IAI), School of Computer Science and Informatics, De Montfort University, United Kingdom.

David ElizondoInstitute of Artificial Intelligence (IAI), School of Computer Science and Informatics, De Montfort University, United Kingdom.

Armaghan MoemeniSchool of Computer Science, University of Nottingham, United Kingdom.

De Montfort University · GBInstituto Tecnológico de Costa Rica · CRUniversity of Nottingham · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-supervised deep learning is an attractive alternative, where unlabelled data is leveraged to improve the overall model's accuracy. However, in real-world usage settings, an unlabelled dataset might present a different distribution than the labelled dataset (i.e. the labelled dataset was sampled from a

Indexed as

Chest X-rayComputer aided diagnosisCovid-19Distribution mismatchMixMatchOut of distribution detectionSemi-supervised deep learning

Identifiers

PMID35573166

DOI10.1016/j.asoc.2022.108983

PMCPMC9085448

OpenAlexW3196904121

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.