Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.

Convolutional neural networks Deep learning Image quality Image reconstruction Transfer learning

Journal

IEEE transactions on radiation and plasma medical sciences
ISSN: 2469-7311
Titre abrégé: IEEE Trans Radiat Plasma Med Sci
Pays: United States
ID NLM: 101705223

Informations de publication

Date de publication:
01 Aug 2024
Historique:
medline: 15 10 2024
pubmed: 15 10 2024
entrez: 15 10 2024
Statut: aheadofprint

Résumé

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 [

Identifiants

pubmed: 39404656
doi: 10.1109/TRPMS.2024.3436697
pmc: PMC7616552
doi:

Types de publication

Journal Article

Langues

eng

Auteurs

Jessica B Hopson (JB)

Department of Biomedical Engineering, King's College London.

Anthime Flaus (A)

King's College London & Guy's and St Thomas' PET Centre, King's College London.

Colm J McGinnity (CJ)

King's College London & Guy's and St Thomas' PET Centre, King's College London.

Radhouene Neji (R)

Department of Biomedical Engineering, King's College London; Siemens Healthcare Limited.

Andrew J Reader (AJ)

Department of Biomedical Engineering, King's College London.

Alexander Hammers (A)

King's College London & Guy's and St Thomas' PET Centre, King's College London.

Classifications MeSH