Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation

The CoAtNet deep neural model has been shown to achieve state-of-the-art performance by stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet apply efficient convolutions for extracting local features out of the input image and the initial fine-resolution fea...

Descripción completa

Detalles Bibliográficos
Autores: Zaidkilani, Nadeem, García García, Miguel Ángel, Puig, Domenec
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/714696
Acceso en línea:http://hdl.handle.net/10486/714696
https://dx.doi.org/10.1007/s00521-024-09963-w
Access Level:acceso abierto
Palabra clave:Breast cancer
CoAtNet
Deep neural networks
Transformers
Ultrasound image segmentation
Telecomunicaciones
id ES_744c8f034c2e81ed2826e640b88a8ac3
oai_identifier_str oai:repositorio.uam.es:10486/714696
network_acronym_str ES
network_name_str España
repository_id_str
spelling Dual-Stream CoAtNet models for accurate breast ultrasound image segmentationZaidkilani, NadeemGarcía García, Miguel ÁngelPuig, DomenecBreast cancerCoAtNetDeep neural networksTransformersUltrasound image segmentationTelecomunicacionesThe CoAtNet deep neural model has been shown to achieve state-of-the-art performance by stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet apply efficient convolutions for extracting local features out of the input image and the initial fine-resolution feature maps. In turn, the final layers apply more cumbersome Transformers in order to extract global features from the coarse-resolution feature maps. The model’s outcome directly depends on those final global features. This paper proposes an extension of the original CoAtNet model based on the introduction of a dual stream of convolution and self-attention blocks applied at the final layers of CoAtNet. In this way, those final layers automatically aggregate both local and global features extracted from the initial feature maps. Two dual-stream topologies have been proposed and evaluated. This Dual-Stream CoAtNet model exhibits a significant improvement on the segmentation accuracy of breast ultrasound images, thus contributing to the development of more robust tumor detection methodsThe Spanish Government partly supported this research through Project TED2021-130081B-C21, and Project PDC2022-133383-I00SpringerDepartamento de Tecnología Electrónica y de las ComunicacionesEscuela Politécnica Superior20242024-05-27research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/714696https://dx.doi.org/10.1007/s00521-024-09963-wreponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7146962026-06-23T12:46:27Z
dc.title.none.fl_str_mv Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
title Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
spellingShingle Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
Zaidkilani, Nadeem
Breast cancer
CoAtNet
Deep neural networks
Transformers
Ultrasound image segmentation
Telecomunicaciones
title_short Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
title_full Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
title_fullStr Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
title_full_unstemmed Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
title_sort Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
dc.creator.none.fl_str_mv Zaidkilani, Nadeem
García García, Miguel Ángel
Puig, Domenec
author Zaidkilani, Nadeem
author_facet Zaidkilani, Nadeem
García García, Miguel Ángel
Puig, Domenec
author_role author
author2 García García, Miguel Ángel
Puig, Domenec
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Tecnología Electrónica y de las Comunicaciones
Escuela Politécnica Superior
dc.subject.none.fl_str_mv Breast cancer
CoAtNet
Deep neural networks
Transformers
Ultrasound image segmentation
Telecomunicaciones
topic Breast cancer
CoAtNet
Deep neural networks
Transformers
Ultrasound image segmentation
Telecomunicaciones
description The CoAtNet deep neural model has been shown to achieve state-of-the-art performance by stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet apply efficient convolutions for extracting local features out of the input image and the initial fine-resolution feature maps. In turn, the final layers apply more cumbersome Transformers in order to extract global features from the coarse-resolution feature maps. The model’s outcome directly depends on those final global features. This paper proposes an extension of the original CoAtNet model based on the introduction of a dual stream of convolution and self-attention blocks applied at the final layers of CoAtNet. In this way, those final layers automatically aggregate both local and global features extracted from the initial feature maps. Two dual-stream topologies have been proposed and evaluated. This Dual-Stream CoAtNet model exhibits a significant improvement on the segmentation accuracy of breast ultrasound images, thus contributing to the development of more robust tumor detection methods
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-05-27
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/714696
https://dx.doi.org/10.1007/s00521-024-09963-w
url http://hdl.handle.net/10486/714696
https://dx.doi.org/10.1007/s00521-024-09963-w
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869410885338923008
score 15,811543

Similares en LA Referencia