A multi-attention and depthwise separable convolution network for medical image segmentation Article Swipe

View

Yuxiang Zhou , Xin Kang , Fuji Ren , Huimin Lu , Satoshi Nakagawa , Shan Xiao ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1016/j.neucom.2023.126970

Automatic medical image segmentation method is highly needed to help experts in lesion segmentation. The deep learning technology emerging has profoundly driven the development of medical image segmentation. While U-Net and attention mechanisms are widely utilized in this field, the application of attention, albeit successful in natural scene image segmentation, tends to inflate the number of model parameters and neglects the potential for feature fusion between different convolutional layers. In response to these challenges, we present the Multi-Attention and Depthwise Separable Convolution U-Net (MDSU-Net), designed to enhance feature extraction. The multi-attention aspect of our framework integrates dual attention and attention gates, adeptly capturing rich contextual details and seamlessly fusing features across diverse convolutional layers. Additionally, our encoder integrates a depthwise separable convolution layer, streamlining the model's complexity without sacrificing its efficacy, ensuring versatility across various segmentation tasks. The results demonstrate that our method outperforms state-of-the-art across three diverse medical image datasets.

Related Topics

Computer Science

Artificial Intelligence

Segmentation Fault

Convolutional Neural Network

Mathematical Analysis

Concepts

Computer science Artificial intelligence Segmentation Convolution (computer science) Convolutional neural network Feature (linguistics) Encoder Image segmentation Pattern recognition (psychology) Field (mathematics) Deep learning Image (mathematics) Feature extraction Separable space Segmentation-based object categorization Scale-space segmentation Computer vision Artificial neural network Mathematics Operating system Philosophy Linguistics Pure mathematics Mathematical analysis

Metadata

Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.neucom.2023.126970
OA Status: hybrid
Cited By: 39
References: 65
Related Works: 10
OpenAlex ID: https://openalex.org/W4388011824

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4388011824

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.1016/j.neucom.2023.126970

Digital Object Identifier
Title: A multi-attention and depthwise separable convolution network for medical image segmentation

Work title
Type: article

OpenAlex work type
Language: en

Primary language
Publication year: 2023

Year of publication
Publication date: 2023-10-29

Full publication date if available
Authors: Yuxiang Zhou, Xin Kang, Fuji Ren, Huimin Lu, Satoshi Nakagawa, Shan Xiao

List of authors in order
Landing page: https://doi.org/10.1016/j.neucom.2023.126970

Publisher landing page
Open access: Yes

Whether a free full text is available
OA status: hybrid

Open access status per OpenAlex
OA URL: https://doi.org/10.1016/j.neucom.2023.126970

Direct OA link when available
Concepts: Computer science, Artificial intelligence, Segmentation, Convolution (computer science), Convolutional neural network, Feature (linguistics), Encoder, Image segmentation, Pattern recognition (psychology), Field (mathematics), Deep learning, Image (mathematics), Feature extraction, Separable space, Segmentation-based object categorization, Scale-space segmentation, Computer vision, Artificial neural network, Mathematics, Operating system, Philosophy, Linguistics, Pure mathematics, Mathematical analysis

Top concepts (fields/topics) attached by OpenAlex
Cited by: 39

Total citation count in OpenAlex
Citations by year (recent): 2025: 24, 2024: 15

Per-year citation counts (last 5 years)
References (count): 65

Number of works referenced by this work
Related works (count): 10

Other works algorithmically related by OpenAlex

Full payload

id	https://openalex.org/W4388011824
doi	https://doi.org/10.1016/j.neucom.2023.126970
ids.doi	https://doi.org/10.1016/j.neucom.2023.126970
ids.openalex	https://openalex.org/W4388011824
fwci	7.09676526
type	article
title	A multi-attention and depthwise separable convolution network for medical image segmentation
awards[0].id	https://openalex.org/G701389233
awards[0].funder_id	https://openalex.org/F4320334764
awards[0].display_name
awards[0].funder_award_id	19K20345
awards[0].funder_display_name	Japan Society for the Promotion of Science
awards[1].id	https://openalex.org/G3684880427
awards[1].funder_id	https://openalex.org/F4320334764
awards[1].display_name
awards[1].funder_award_id	19H04215
awards[1].funder_display_name	Japan Society for the Promotion of Science
biblio.issue
biblio.volume	564
biblio.last_page	126970
biblio.first_page	126970
topics[0].id	https://openalex.org/T10036
topics[0].field.id	https://openalex.org/fields/17
topics[0].field.display_name	Computer Science
topics[0].score	0.9997000098228455
topics[0].domain.id	https://openalex.org/domains/3
topics[0].domain.display_name	Physical Sciences
topics[0].subfield.id	https://openalex.org/subfields/1707
topics[0].subfield.display_name	Computer Vision and Pattern Recognition
topics[0].display_name	Advanced Neural Network Applications
topics[1].id	https://openalex.org/T12422
topics[1].field.id	https://openalex.org/fields/27
topics[1].field.display_name	Medicine
topics[1].score	0.9991999864578247
topics[1].domain.id	https://openalex.org/domains/4
topics[1].domain.display_name	Health Sciences
topics[1].subfield.id	https://openalex.org/subfields/2741
topics[1].subfield.display_name	Radiology, Nuclear Medicine and Imaging
topics[1].display_name	Radiomics and Machine Learning in Medical Imaging
topics[2].id	https://openalex.org/T11775
topics[2].field.id	https://openalex.org/fields/27
topics[2].field.display_name	Medicine
topics[2].score	0.9987000226974487
topics[2].domain.id	https://openalex.org/domains/4
topics[2].domain.display_name	Health Sciences
topics[2].subfield.id	https://openalex.org/subfields/2741
topics[2].subfield.display_name	Radiology, Nuclear Medicine and Imaging
topics[2].display_name	COVID-19 diagnosis using AI
funders[0].id	https://openalex.org/F4320334764
funders[0].ror	https://ror.org/00hhkn466
funders[0].display_name	Japan Society for the Promotion of Science
is_xpac	False
apc_list.value	2470
apc_list.currency	USD
apc_list.value_usd	2470
apc_paid.value	2470
apc_paid.currency	USD
apc_paid.value_usd	2470
concepts[0].id	https://openalex.org/C41008148
concepts[0].level	0
concepts[0].score	0.8420612812042236
concepts[0].wikidata	https://www.wikidata.org/wiki/Q21198
concepts[0].display_name	Computer science
concepts[1].id	https://openalex.org/C154945302
concepts[1].level	1
concepts[1].score	0.748771607875824
concepts[1].wikidata	https://www.wikidata.org/wiki/Q11660
concepts[1].display_name	Artificial intelligence
concepts[2].id	https://openalex.org/C89600930
concepts[2].level	2
concepts[2].score	0.7465344071388245
concepts[2].wikidata	https://www.wikidata.org/wiki/Q1423946
concepts[2].display_name	Segmentation
concepts[3].id	https://openalex.org/C45347329
concepts[3].level	3
concepts[3].score	0.6791703701019287
concepts[3].wikidata	https://www.wikidata.org/wiki/Q5166604
concepts[3].display_name	Convolution (computer science)
concepts[4].id	https://openalex.org/C81363708
concepts[4].level	2
concepts[4].score	0.5754817724227905
concepts[4].wikidata	https://www.wikidata.org/wiki/Q17084460
concepts[4].display_name	Convolutional neural network
concepts[5].id	https://openalex.org/C2776401178
concepts[5].level	2
concepts[5].score	0.530530571937561
concepts[5].wikidata	https://www.wikidata.org/wiki/Q12050496
concepts[5].display_name	Feature (linguistics)
concepts[6].id	https://openalex.org/C118505674
concepts[6].level	2
concepts[6].score	0.5122389197349548
concepts[6].wikidata	https://www.wikidata.org/wiki/Q42586063
concepts[6].display_name	Encoder
concepts[7].id	https://openalex.org/C124504099
concepts[7].level	3
concepts[7].score	0.5109056830406189
concepts[7].wikidata	https://www.wikidata.org/wiki/Q56933
concepts[7].display_name	Image segmentation
concepts[8].id	https://openalex.org/C153180895
concepts[8].level	2
concepts[8].score	0.49670034646987915
concepts[8].wikidata	https://www.wikidata.org/wiki/Q7148389
concepts[8].display_name	Pattern recognition (psychology)
concepts[9].id	https://openalex.org/C9652623
concepts[9].level	2
concepts[9].score	0.4714570641517639
concepts[9].wikidata	https://www.wikidata.org/wiki/Q190109
concepts[9].display_name	Field (mathematics)
concepts[10].id	https://openalex.org/C108583219
concepts[10].level	2
concepts[10].score	0.470829039812088
concepts[10].wikidata	https://www.wikidata.org/wiki/Q197536
concepts[10].display_name	Deep learning
concepts[11].id	https://openalex.org/C115961682
concepts[11].level	2
concepts[11].score	0.46172088384628296
concepts[11].wikidata	https://www.wikidata.org/wiki/Q860623
concepts[11].display_name	Image (mathematics)
concepts[12].id	https://openalex.org/C52622490
concepts[12].level	2
concepts[12].score	0.43342986702919006
concepts[12].wikidata	https://www.wikidata.org/wiki/Q1026626
concepts[12].display_name	Feature extraction
concepts[13].id	https://openalex.org/C70710897
concepts[13].level	2
concepts[13].score	0.42707863450050354
concepts[13].wikidata	https://www.wikidata.org/wiki/Q680081
concepts[13].display_name	Separable space
concepts[14].id	https://openalex.org/C25694479
concepts[14].level	5
concepts[14].score	0.4186931848526001
concepts[14].wikidata	https://www.wikidata.org/wiki/Q7446278
concepts[14].display_name	Segmentation-based object categorization
concepts[15].id	https://openalex.org/C65885262
concepts[15].level	4
concepts[15].score	0.40638887882232666
concepts[15].wikidata	https://www.wikidata.org/wiki/Q7429708
concepts[15].display_name	Scale-space segmentation
concepts[16].id	https://openalex.org/C31972630
concepts[16].level	1
concepts[16].score	0.39919692277908325
concepts[16].wikidata	https://www.wikidata.org/wiki/Q844240
concepts[16].display_name	Computer vision
concepts[17].id	https://openalex.org/C50644808
concepts[17].level	2
concepts[17].score	0.2409362494945526
concepts[17].wikidata	https://www.wikidata.org/wiki/Q192776
concepts[17].display_name	Artificial neural network
concepts[18].id	https://openalex.org/C33923547
concepts[18].level	0
concepts[18].score	0.07069534063339233
concepts[18].wikidata	https://www.wikidata.org/wiki/Q395
concepts[18].display_name	Mathematics
concepts[19].id	https://openalex.org/C111919701
concepts[19].level	1
concepts[19].score	0.0
concepts[19].wikidata	https://www.wikidata.org/wiki/Q9135
concepts[19].display_name	Operating system
concepts[20].id	https://openalex.org/C138885662
concepts[20].level	0
concepts[20].score	0.0
concepts[20].wikidata	https://www.wikidata.org/wiki/Q5891
concepts[20].display_name	Philosophy
concepts[21].id	https://openalex.org/C41895202
concepts[21].level	1
concepts[21].score	0.0
concepts[21].wikidata	https://www.wikidata.org/wiki/Q8162
concepts[21].display_name	Linguistics
concepts[22].id	https://openalex.org/C202444582
concepts[22].level	1
concepts[22].score	0.0
concepts[22].wikidata	https://www.wikidata.org/wiki/Q837863
concepts[22].display_name	Pure mathematics
concepts[23].id	https://openalex.org/C134306372
concepts[23].level	1
concepts[23].score	0.0
concepts[23].wikidata	https://www.wikidata.org/wiki/Q7754
concepts[23].display_name	Mathematical analysis
keywords[0].id	https://openalex.org/keywords/computer-science
keywords[0].score	0.8420612812042236
keywords[0].display_name	Computer science
keywords[1].id	https://openalex.org/keywords/artificial-intelligence
keywords[1].score	0.748771607875824
keywords[1].display_name	Artificial intelligence
keywords[2].id	https://openalex.org/keywords/segmentation
keywords[2].score	0.7465344071388245
keywords[2].display_name	Segmentation
keywords[3].id	https://openalex.org/keywords/convolution
keywords[3].score	0.6791703701019287
keywords[3].display_name	Convolution (computer science)
keywords[4].id	https://openalex.org/keywords/convolutional-neural-network
keywords[4].score	0.5754817724227905
keywords[4].display_name	Convolutional neural network
keywords[5].id	https://openalex.org/keywords/feature
keywords[5].score	0.530530571937561
keywords[5].display_name	Feature (linguistics)
keywords[6].id	https://openalex.org/keywords/encoder
keywords[6].score	0.5122389197349548
keywords[6].display_name	Encoder
keywords[7].id	https://openalex.org/keywords/image-segmentation
keywords[7].score	0.5109056830406189
keywords[7].display_name	Image segmentation
keywords[8].id	https://openalex.org/keywords/pattern-recognition
keywords[8].score	0.49670034646987915
keywords[8].display_name	Pattern recognition (psychology)
keywords[9].id	https://openalex.org/keywords/field
keywords[9].score	0.4714570641517639
keywords[9].display_name	Field (mathematics)
keywords[10].id	https://openalex.org/keywords/deep-learning
keywords[10].score	0.470829039812088
keywords[10].display_name	Deep learning
keywords[11].id	https://openalex.org/keywords/image
keywords[11].score	0.46172088384628296
keywords[11].display_name	Image (mathematics)
keywords[12].id	https://openalex.org/keywords/feature-extraction
keywords[12].score	0.43342986702919006
keywords[12].display_name	Feature extraction
keywords[13].id	https://openalex.org/keywords/separable-space
keywords[13].score	0.42707863450050354
keywords[13].display_name	Separable space
keywords[14].id	https://openalex.org/keywords/segmentation-based-object-categorization
keywords[14].score	0.4186931848526001
keywords[14].display_name	Segmentation-based object categorization
keywords[15].id	https://openalex.org/keywords/scale-space-segmentation
keywords[15].score	0.40638887882232666
keywords[15].display_name	Scale-space segmentation
keywords[16].id	https://openalex.org/keywords/computer-vision
keywords[16].score	0.39919692277908325
keywords[16].display_name	Computer vision
keywords[17].id	https://openalex.org/keywords/artificial-neural-network
keywords[17].score	0.2409362494945526
keywords[17].display_name	Artificial neural network
keywords[18].id	https://openalex.org/keywords/mathematics
keywords[18].score	0.07069534063339233
keywords[18].display_name	Mathematics
language	en
locations[0].id	doi:10.1016/j.neucom.2023.126970
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S45693802
locations[0].source.issn	0925-2312, 1872-8286
locations[0].source.type	journal
locations[0].source.is_oa	False
locations[0].source.issn_l	0925-2312
locations[0].source.is_core	True
locations[0].source.is_in_doaj	False
locations[0].source.display_name	Neurocomputing
locations[0].source.host_organization	https://openalex.org/P4310320990
locations[0].source.host_organization_name	Elsevier BV
locations[0].source.host_organization_lineage	https://openalex.org/P4310320990
locations[0].source.host_organization_lineage_names	Elsevier BV
locations[0].license	cc-by-nc-nd
locations[0].pdf_url
locations[0].version	publishedVersion
locations[0].raw_type	journal-article
locations[0].license_id	https://openalex.org/licenses/cc-by-nc-nd
locations[0].is_accepted	True
locations[0].is_published	True
locations[0].raw_source_name	Neurocomputing
locations[0].landing_page_url	https://doi.org/10.1016/j.neucom.2023.126970
indexed_in	crossref
authorships[0].author.id	https://openalex.org/A5038656568
authorships[0].author.orcid	https://orcid.org/0000-0001-7538-7754
authorships[0].author.display_name	Yuxiang Zhou
authorships[0].countries	JP
authorships[0].affiliations[0].institution_ids	https://openalex.org/I922474255
authorships[0].affiliations[0].raw_affiliation_string	Tokushima University, Tokushima, Japan
authorships[0].institutions[0].id	https://openalex.org/I922474255
authorships[0].institutions[0].ror	https://ror.org/044vy1d05
authorships[0].institutions[0].type	education
authorships[0].institutions[0].lineage	https://openalex.org/I922474255
authorships[0].institutions[0].country_code	JP
authorships[0].institutions[0].display_name	Tokushima University
authorships[0].author_position	first
authorships[0].raw_author_name	Yuxiang Zhou
authorships[0].is_corresponding	False
authorships[0].raw_affiliation_strings	Tokushima University, Tokushima, Japan
authorships[1].author.id	https://openalex.org/A5031861539
authorships[1].author.orcid	https://orcid.org/0000-0001-6024-3598
authorships[1].author.display_name	Xin Kang
authorships[1].countries	JP
authorships[1].affiliations[0].institution_ids	https://openalex.org/I922474255
authorships[1].affiliations[0].raw_affiliation_string	Tokushima University, Tokushima, Japan
authorships[1].institutions[0].id	https://openalex.org/I922474255
authorships[1].institutions[0].ror	https://ror.org/044vy1d05
authorships[1].institutions[0].type	education
authorships[1].institutions[0].lineage	https://openalex.org/I922474255
authorships[1].institutions[0].country_code	JP
authorships[1].institutions[0].display_name	Tokushima University
authorships[1].author_position	middle
authorships[1].raw_author_name	Xin Kang
authorships[1].is_corresponding	True
authorships[1].raw_affiliation_strings	Tokushima University, Tokushima, Japan
authorships[2].author.id	https://openalex.org/A5071943346
authorships[2].author.orcid	https://orcid.org/0000-0003-4860-9184
authorships[2].author.display_name	Fuji Ren
authorships[2].countries	CN
authorships[2].affiliations[0].institution_ids	https://openalex.org/I150229711
authorships[2].affiliations[0].raw_affiliation_string	University of Electronic Science and Technology of China, Chengdu, China
authorships[2].institutions[0].id	https://openalex.org/I150229711
authorships[2].institutions[0].ror	https://ror.org/04qr3zq92
authorships[2].institutions[0].type	education
authorships[2].institutions[0].lineage	https://openalex.org/I150229711
authorships[2].institutions[0].country_code	CN
authorships[2].institutions[0].display_name	University of Electronic Science and Technology of China
authorships[2].author_position	middle
authorships[2].raw_author_name	Fuji Ren
authorships[2].is_corresponding	False
authorships[2].raw_affiliation_strings	University of Electronic Science and Technology of China, Chengdu, China
authorships[3].author.id	https://openalex.org/A5019035804
authorships[3].author.orcid	https://orcid.org/0000-0001-9794-3221
authorships[3].author.display_name	Huimin Lu
authorships[3].countries	JP
authorships[3].affiliations[0].institution_ids	https://openalex.org/I207014233
authorships[3].affiliations[0].raw_affiliation_string	Kyushu Institute of Technology, Kitakyushu, Japan
authorships[3].institutions[0].id	https://openalex.org/I207014233
authorships[3].institutions[0].ror	https://ror.org/02278tr80
authorships[3].institutions[0].type	education
authorships[3].institutions[0].lineage	https://openalex.org/I207014233
authorships[3].institutions[0].country_code	JP
authorships[3].institutions[0].display_name	Kyushu Institute of Technology
authorships[3].author_position	middle
authorships[3].raw_author_name	Huimin Lu
authorships[3].is_corresponding	False
authorships[3].raw_affiliation_strings	Kyushu Institute of Technology, Kitakyushu, Japan
authorships[4].author.id	https://openalex.org/A5045676644
authorships[4].author.orcid	https://orcid.org/0000-0002-6293-0428
authorships[4].author.display_name	Satoshi Nakagawa
authorships[4].countries	JP
authorships[4].affiliations[0].institution_ids	https://openalex.org/I74801974
authorships[4].affiliations[0].raw_affiliation_string	The University of Tokyo, Tokyo, Japan
authorships[4].institutions[0].id	https://openalex.org/I74801974
authorships[4].institutions[0].ror	https://ror.org/057zh3y96
authorships[4].institutions[0].type	education
authorships[4].institutions[0].lineage	https://openalex.org/I74801974
authorships[4].institutions[0].country_code	JP
authorships[4].institutions[0].display_name	The University of Tokyo
authorships[4].author_position	middle
authorships[4].raw_author_name	Satoshi Nakagawa
authorships[4].is_corresponding	False
authorships[4].raw_affiliation_strings	The University of Tokyo, Tokyo, Japan
authorships[5].author.id	https://openalex.org/A5002135520
authorships[5].author.orcid	https://orcid.org/0000-0002-8140-8512
authorships[5].author.display_name	Shan Xiao
authorships[5].countries	JP
authorships[5].affiliations[0].institution_ids	https://openalex.org/I4210122944
authorships[5].affiliations[0].raw_affiliation_string	Tokushima University Hospital, Tokushima, Japan
authorships[5].institutions[0].id	https://openalex.org/I4210122944
authorships[5].institutions[0].ror	https://ror.org/021ph5e41
authorships[5].institutions[0].type	healthcare
authorships[5].institutions[0].lineage	https://openalex.org/I4210122944
authorships[5].institutions[0].country_code	JP
authorships[5].institutions[0].display_name	Tokushima University Hospital
authorships[5].author_position	last
authorships[5].raw_author_name	Xiao Shan
authorships[5].is_corresponding	False
authorships[5].raw_affiliation_strings	Tokushima University Hospital, Tokushima, Japan
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://doi.org/10.1016/j.neucom.2023.126970
open_access.oa_status	hybrid
open_access.any_repository_has_fulltext	False
created_date	2025-10-10T00:00:00
display_name	A multi-attention and depthwise separable convolution network for medical image segmentation
has_fulltext	False
is_retracted	False
updated_date	2025-11-06T03:46:38.306776
primary_topic.id	https://openalex.org/T10036
primary_topic.field.id	https://openalex.org/fields/17
primary_topic.field.display_name	Computer Science
primary_topic.score	0.9997000098228455
primary_topic.domain.id	https://openalex.org/domains/3
primary_topic.domain.display_name	Physical Sciences
primary_topic.subfield.id	https://openalex.org/subfields/1707
primary_topic.subfield.display_name	Computer Vision and Pattern Recognition
primary_topic.display_name	Advanced Neural Network Applications
related_works	https://openalex.org/W2530952058, https://openalex.org/W2582836483, https://openalex.org/W4299366318, https://openalex.org/W3158431807, https://openalex.org/W4297580547, https://openalex.org/W2951583185, https://openalex.org/W3144569342, https://openalex.org/W2185902295, https://openalex.org/W4308155352, https://openalex.org/W2945274617
cited_by_count	39
counts_by_year[0].year	2025
counts_by_year[0].cited_by_count	24
counts_by_year[1].year	2024
counts_by_year[1].cited_by_count	15
locations_count	1
best_oa_location.id	doi:10.1016/j.neucom.2023.126970
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S45693802
best_oa_location.source.issn	0925-2312, 1872-8286
best_oa_location.source.type	journal
best_oa_location.source.is_oa	False
best_oa_location.source.issn_l	0925-2312
best_oa_location.source.is_core	True
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	Neurocomputing
best_oa_location.source.host_organization	https://openalex.org/P4310320990
best_oa_location.source.host_organization_name	Elsevier BV
best_oa_location.source.host_organization_lineage	https://openalex.org/P4310320990
best_oa_location.source.host_organization_lineage_names	Elsevier BV
best_oa_location.license	cc-by-nc-nd
best_oa_location.pdf_url
best_oa_location.version	publishedVersion
best_oa_location.raw_type	journal-article
best_oa_location.license_id	https://openalex.org/licenses/cc-by-nc-nd
best_oa_location.is_accepted	True
best_oa_location.is_published	True
best_oa_location.raw_source_name	Neurocomputing
best_oa_location.landing_page_url	https://doi.org/10.1016/j.neucom.2023.126970
primary_location.id	doi:10.1016/j.neucom.2023.126970
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S45693802
primary_location.source.issn	0925-2312, 1872-8286
primary_location.source.type	journal
primary_location.source.is_oa	False
primary_location.source.issn_l	0925-2312
primary_location.source.is_core	True
primary_location.source.is_in_doaj	False
primary_location.source.display_name	Neurocomputing
primary_location.source.host_organization	https://openalex.org/P4310320990
primary_location.source.host_organization_name	Elsevier BV
primary_location.source.host_organization_lineage	https://openalex.org/P4310320990
primary_location.source.host_organization_lineage_names	Elsevier BV
primary_location.license	cc-by-nc-nd
primary_location.pdf_url
primary_location.version	publishedVersion
primary_location.raw_type	journal-article
primary_location.license_id	https://openalex.org/licenses/cc-by-nc-nd
primary_location.is_accepted	True
primary_location.is_published	True
primary_location.raw_source_name	Neurocomputing
primary_location.landing_page_url	https://doi.org/10.1016/j.neucom.2023.126970
publication_date	2023-10-29
publication_year	2023
referenced_works	https://openalex.org/W4220670656, https://openalex.org/W3027763298, https://openalex.org/W4285098962, https://openalex.org/W2791723176, https://openalex.org/W3002569343, https://openalex.org/W4292136262, https://openalex.org/W4282934095, https://openalex.org/W4386057777, https://openalex.org/W4292183902, https://openalex.org/W6805172049, https://openalex.org/W6639824700, https://openalex.org/W6796370697, https://openalex.org/W3009475780, https://openalex.org/W6798616986, https://openalex.org/W2531409750, https://openalex.org/W6739901393, https://openalex.org/W3146366485, https://openalex.org/W4327520754, https://openalex.org/W6791241316, https://openalex.org/W3182906273, https://openalex.org/W4381384742, https://openalex.org/W6765842094, https://openalex.org/W2994739204, https://openalex.org/W6846216940, https://openalex.org/W4366124369, https://openalex.org/W3197440202, https://openalex.org/W4315865046, https://openalex.org/W6795435739, https://openalex.org/W4212875960, https://openalex.org/W6791469159, https://openalex.org/W2793268137, https://openalex.org/W2567599812, https://openalex.org/W3096947210, https://openalex.org/W3011743383, https://openalex.org/W6768712932, https://openalex.org/W6769827063, https://openalex.org/W6768908150, https://openalex.org/W6761415830, https://openalex.org/W3206685025, https://openalex.org/W6758951285, https://openalex.org/W3017153481, https://openalex.org/W6786346207, https://openalex.org/W2982083293, https://openalex.org/W2963163009, https://openalex.org/W2194775991, https://openalex.org/W2888358068, https://openalex.org/W6718240422, https://openalex.org/W6766121741, https://openalex.org/W4322627140, https://openalex.org/W6770229101, https://openalex.org/W2559597482, https://openalex.org/W4317515509, https://openalex.org/W2927980542, https://openalex.org/W4300154369, https://openalex.org/W4310330006, https://openalex.org/W3168491317, https://openalex.org/W2798122215, https://openalex.org/W3029253863, https://openalex.org/W3127751679, https://openalex.org/W2612690371, https://openalex.org/W4297775537, https://openalex.org/W1522301498, https://openalex.org/W4253570578, https://openalex.org/W3003864320, https://openalex.org/W4206816857
referenced_works_count	65
abstract_inverted_index.a	118
abstract_inverted_index.In	69
abstract_inverted_index.in	11, 36, 45
abstract_inverted_index.is	5
abstract_inverted_index.of	24, 41, 55, 92
abstract_inverted_index.to	8, 51, 71, 85
abstract_inverted_index.we	74
abstract_inverted_index.The	14, 89, 137
abstract_inverted_index.and	30, 58, 78, 98, 106
abstract_inverted_index.are	33
abstract_inverted_index.for	62
abstract_inverted_index.has	19
abstract_inverted_index.its	129
abstract_inverted_index.our	93, 115, 141
abstract_inverted_index.the	22, 39, 53, 60, 76, 124
abstract_inverted_index.deep	15
abstract_inverted_index.dual	96
abstract_inverted_index.help	9
abstract_inverted_index.rich	103
abstract_inverted_index.that	140
abstract_inverted_index.this	37
abstract_inverted_index.U-Net	29, 82
abstract_inverted_index.While	28
abstract_inverted_index.image	2, 26, 48, 149
abstract_inverted_index.model	56
abstract_inverted_index.scene	47
abstract_inverted_index.tends	50
abstract_inverted_index.these	72
abstract_inverted_index.three	146
abstract_inverted_index.across	110, 133, 145
abstract_inverted_index.albeit	43
abstract_inverted_index.aspect	91
abstract_inverted_index.driven	21
abstract_inverted_index.field,	38
abstract_inverted_index.fusing	108
abstract_inverted_index.fusion	64
abstract_inverted_index.gates,	100
abstract_inverted_index.highly	6
abstract_inverted_index.layer,	122
abstract_inverted_index.lesion	12
abstract_inverted_index.method	4, 142
abstract_inverted_index.needed	7
abstract_inverted_index.number	54
abstract_inverted_index.tasks.	136
abstract_inverted_index.widely	34
abstract_inverted_index.adeptly	101
abstract_inverted_index.between	65
abstract_inverted_index.details	105
abstract_inverted_index.diverse	111, 147
abstract_inverted_index.encoder	116
abstract_inverted_index.enhance	86
abstract_inverted_index.experts	10
abstract_inverted_index.feature	63, 87
abstract_inverted_index.inflate	52
abstract_inverted_index.layers.	68, 113
abstract_inverted_index.medical	1, 25, 148
abstract_inverted_index.model's	125
abstract_inverted_index.natural	46
abstract_inverted_index.present	75
abstract_inverted_index.results	138
abstract_inverted_index.various	134
abstract_inverted_index.without	127
abstract_inverted_index.designed	84
abstract_inverted_index.emerging	18
abstract_inverted_index.ensuring	131
abstract_inverted_index.features	109
abstract_inverted_index.learning	16
abstract_inverted_index.neglects	59
abstract_inverted_index.response	70
abstract_inverted_index.utilized	35
abstract_inverted_index.Automatic	0
abstract_inverted_index.Depthwise	79
abstract_inverted_index.Separable	80
abstract_inverted_index.attention	31, 97, 99
abstract_inverted_index.capturing	102
abstract_inverted_index.datasets.	150
abstract_inverted_index.depthwise	119
abstract_inverted_index.different	66
abstract_inverted_index.efficacy,	130
abstract_inverted_index.framework	94
abstract_inverted_index.potential	61
abstract_inverted_index.separable	120
abstract_inverted_index.attention,	42
abstract_inverted_index.complexity	126
abstract_inverted_index.contextual	104
abstract_inverted_index.integrates	95, 117
abstract_inverted_index.mechanisms	32
abstract_inverted_index.parameters	57
abstract_inverted_index.profoundly	20
abstract_inverted_index.seamlessly	107
abstract_inverted_index.successful	44
abstract_inverted_index.technology	17
abstract_inverted_index.(MDSU-Net),	83
abstract_inverted_index.Convolution	81
abstract_inverted_index.application	40
abstract_inverted_index.challenges,	73
abstract_inverted_index.convolution	121
abstract_inverted_index.demonstrate	139
abstract_inverted_index.development	23
abstract_inverted_index.extraction.	88
abstract_inverted_index.outperforms	143
abstract_inverted_index.sacrificing	128
abstract_inverted_index.versatility	132
abstract_inverted_index.segmentation	3, 135
abstract_inverted_index.streamlining	123
abstract_inverted_index.Additionally,	114
abstract_inverted_index.convolutional	67, 112
abstract_inverted_index.segmentation,	49
abstract_inverted_index.segmentation.	13, 27
abstract_inverted_index.Multi-Attention	77
abstract_inverted_index.multi-attention	90
abstract_inverted_index.state-of-the-art	144
cited_by_percentile_year.max	100
cited_by_percentile_year.min	99
corresponding_author_ids	https://openalex.org/A5031861539
countries_distinct_count	2
institutions_distinct_count	6
corresponding_institution_ids	https://openalex.org/I922474255
citation_normalized_percentile.value	0.97171656
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True