Evidence map›Paper›PMID 39404656›Full record

ArticleIEEE transactions on radiation and plasma medical sciences2024

Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.

Jessica B Hopson, Anthime Flaus, Colm J McGinnity, Radhouene Neji, Andrew J Reader, Alexander Hammers

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE transactions on radiation and plasma medical sciences, 2024. 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

6 authors.

Jessica B HopsonDepartment of Biomedical Engineering, King's College London.
Anthime FlausKing's College London & Guy's and St Thomas' PET Centre, King's College London.
Colm J McGinnityKing's College London & Guy's and St Thomas' PET Centre, King's College London.
Radhouene NejiDepartment of Biomedical Engineering, King's College London; Siemens Healthcare Limited.
Andrew J Reader *Department of Biomedical Engineering, King's College London.
Alexander Hammers *King's College London & Guy's and St Thomas' PET Centre, King's College London.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically-annotated medical images. Many two-dimensional pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [

Indexed as

Convolutional neural networksDeep learningImage qualityImage reconstructionTransfer learning

Identifiers

PMID39404656
PMCPMC7616552

What Socratic 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 Socratic graph.