DPAM-PSPNet: ultrasonic image segmentation of thyroid nodule based on dual-path attention mechanism Article Swipe
Shuhuan Wang
,
Zhiqing Li
,
Lingmin Liao
,
Chunquan Zhang
,
Jiali Zhao
,
Liang Sang
,
Wei Qian
,
Guangyao Pan
,
Long Huang
,
He Ma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ace6f1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ace6f1
Objective. Deep learning has demonstrated its versatility in the medical field, particularly in medical image segmentation, image classification, and other forms of automated diagnostics. The clinical diagnosis of thyroid nodules requires radiologists to locate nodules, diagnose conditions based on nodule boundaries, textures and their experience. This task is labor-intensive and tiring; therefore, an automated system for accurate thyroid nodule segmentation is essential. In this study, a model named DPAM-PSPNet was proposed, which automatically segments nodules in thyroid ultrasound images and enables to segment malignant nodules precisely. Approach. In this paper, accurate segmentation of nodule edges is achieved by introducing the dual path attention mechanism (DPAM) in PSPNet. In one channel, it captures global information with a lightweight cross-channel interaction mechanism. In other channel, it focus on nodal margins and surrounding information through the residual bridge network. We also updated the integrated loss function to accommodate the DPAM-PSPNet. Main results. The DPAM-PSPNet was tested against the classical segmentation model. Ablation experiments were designed for the two-path attention mechanism and the new loss function, and generalization experiments were designed on the public dataset. Our experimental results demonstrate that DPAM-PSPNet outperforms other existing methods in various evaluation metrics. In the model comparison experiments, it achieved performance with an mIOU of 0.8675, mPA of 0.9357, mPrecision of 0.9202, and Dice coefficient of 0.9213. Significance. The DPAM-PSPNet model can segment thyroid nodules in ultrasound images with little training data and generate accurate boundary regions for these nodules.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/ace6f1
- https://iopscience.iop.org/article/10.1088/1361-6560/ace6f1/pdf
- OA Status
- bronze
- Cited By
- 12
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384009057
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4384009057Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/ace6f1Digital Object Identifier
- Title
-
DPAM-PSPNet: ultrasonic image segmentation of thyroid nodule based on dual-path attention mechanismWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-12Full publication date if available
- Authors
-
Shuhuan Wang, Zhiqing Li, Lingmin Liao, Chunquan Zhang, Jiali Zhao, Liang Sang, Wei Qian, Guangyao Pan, Long Huang, He MaList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/ace6f1Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1361-6560/ace6f1/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1361-6560/ace6f1/pdfDirect OA link when available
- Concepts
-
Thyroid nodules, Segmentation, Artificial intelligence, Nodule (geology), Computer science, Path (computing), Image segmentation, Feature (linguistics), Computer vision, Pattern recognition (psychology), Medicine, Thyroid, Programming language, Internal medicine, Linguistics, Paleontology, Philosophy, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 8Per-year citation counts (last 5 years)
- References (count)
-
67Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4384009057 |
|---|---|
| doi | https://doi.org/10.1088/1361-6560/ace6f1 |
| ids.doi | https://doi.org/10.1088/1361-6560/ace6f1 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37437581 |
| ids.openalex | https://openalex.org/W4384009057 |
| fwci | 3.70865958 |
| type | article |
| title | DPAM-PSPNet: ultrasonic image segmentation of thyroid nodule based on dual-path attention mechanism |
| awards[0].id | https://openalex.org/G8679019133 |
| awards[0].funder_id | https://openalex.org/F4320327780 |
| awards[0].display_name | |
| awards[0].funder_award_id | 20192ACB70013 AND 20181ACG70011 |
| awards[0].funder_display_name | Key Research and Development Program of Jiangxi Province |
| awards[1].id | https://openalex.org/G7446635131 |
| awards[1].funder_id | https://openalex.org/F4320323086 |
| awards[1].display_name | |
| awards[1].funder_award_id | 2022-YGJC-52 |
| awards[1].funder_display_name | Natural Science Foundation of Liaoning Province |
| biblio.issue | 16 |
| biblio.volume | 68 |
| biblio.last_page | 165002 |
| biblio.first_page | 165002 |
| topics[0].id | https://openalex.org/T12422 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9889000058174133 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Radiomics and Machine Learning in Medical Imaging |
| topics[1].id | https://openalex.org/T11636 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.984499990940094 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2718 |
| topics[1].subfield.display_name | Health Informatics |
| topics[1].display_name | Artificial Intelligence in Healthcare and Education |
| topics[2].id | https://openalex.org/T10862 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9735000133514404 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | AI in cancer detection |
| funders[0].id | https://openalex.org/F4320323086 |
| funders[0].ror | |
| funders[0].display_name | Natural Science Foundation of Liaoning Province |
| funders[1].id | https://openalex.org/F4320327780 |
| funders[1].ror | |
| funders[1].display_name | Key Research and Development Program of Jiangxi Province |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2779022025 |
| concepts[0].level | 3 |
| concepts[0].score | 0.6722062826156616 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q53829 |
| concepts[0].display_name | Thyroid nodules |
| concepts[1].id | https://openalex.org/C89600930 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6639398336410522 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[1].display_name | Segmentation |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.592008650302887 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C2776731575 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5711430311203003 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2916245 |
| concepts[3].display_name | Nodule (geology) |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.5068026185035706 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C2777735758 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4856168329715729 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q817765 |
| concepts[5].display_name | Path (computing) |
| concepts[6].id | https://openalex.org/C124504099 |
| concepts[6].level | 3 |
| concepts[6].score | 0.45925232768058777 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[6].display_name | Image segmentation |
| concepts[7].id | https://openalex.org/C2776401178 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4533786177635193 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[7].display_name | Feature (linguistics) |
| concepts[8].id | https://openalex.org/C31972630 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4251210689544678 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[8].display_name | Computer vision |
| concepts[9].id | https://openalex.org/C153180895 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4222533702850342 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[9].display_name | Pattern recognition (psychology) |
| concepts[10].id | https://openalex.org/C71924100 |
| concepts[10].level | 0 |
| concepts[10].score | 0.3225345313549042 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[10].display_name | Medicine |
| concepts[11].id | https://openalex.org/C526584372 |
| concepts[11].level | 2 |
| concepts[11].score | 0.2043527364730835 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q16399 |
| concepts[11].display_name | Thyroid |
| concepts[12].id | https://openalex.org/C199360897 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[12].display_name | Programming language |
| concepts[13].id | https://openalex.org/C126322002 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[13].display_name | Internal medicine |
| concepts[14].id | https://openalex.org/C41895202 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[14].display_name | Linguistics |
| concepts[15].id | https://openalex.org/C151730666 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[15].display_name | Paleontology |
| concepts[16].id | https://openalex.org/C138885662 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[16].display_name | Philosophy |
| concepts[17].id | https://openalex.org/C86803240 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[17].display_name | Biology |
| keywords[0].id | https://openalex.org/keywords/thyroid-nodules |
| keywords[0].score | 0.6722062826156616 |
| keywords[0].display_name | Thyroid nodules |
| keywords[1].id | https://openalex.org/keywords/segmentation |
| keywords[1].score | 0.6639398336410522 |
| keywords[1].display_name | Segmentation |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.592008650302887 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/nodule |
| keywords[3].score | 0.5711430311203003 |
| keywords[3].display_name | Nodule (geology) |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.5068026185035706 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/path |
| keywords[5].score | 0.4856168329715729 |
| keywords[5].display_name | Path (computing) |
| keywords[6].id | https://openalex.org/keywords/image-segmentation |
| keywords[6].score | 0.45925232768058777 |
| keywords[6].display_name | Image segmentation |
| keywords[7].id | https://openalex.org/keywords/feature |
| keywords[7].score | 0.4533786177635193 |
| keywords[7].display_name | Feature (linguistics) |
| keywords[8].id | https://openalex.org/keywords/computer-vision |
| keywords[8].score | 0.4251210689544678 |
| keywords[8].display_name | Computer vision |
| keywords[9].id | https://openalex.org/keywords/pattern-recognition |
| keywords[9].score | 0.4222533702850342 |
| keywords[9].display_name | Pattern recognition (psychology) |
| keywords[10].id | https://openalex.org/keywords/medicine |
| keywords[10].score | 0.3225345313549042 |
| keywords[10].display_name | Medicine |
| keywords[11].id | https://openalex.org/keywords/thyroid |
| keywords[11].score | 0.2043527364730835 |
| keywords[11].display_name | Thyroid |
| language | en |
| locations[0].id | doi:10.1088/1361-6560/ace6f1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S20241394 |
| locations[0].source.issn | 0031-9155, 1361-6560 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0031-9155 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Physics in Medicine and Biology |
| locations[0].source.host_organization | https://openalex.org/P4310320083 |
| locations[0].source.host_organization_name | IOP Publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| locations[0].source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| locations[0].license | |
| locations[0].pdf_url | https://iopscience.iop.org/article/10.1088/1361-6560/ace6f1/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Physics in Medicine & Biology |
| locations[0].landing_page_url | https://doi.org/10.1088/1361-6560/ace6f1 |
| locations[1].id | pmid:37437581 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Physics in medicine and biology |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37437581 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5035118619 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1699-3612 |
| authorships[0].author.display_name | Shuhuan Wang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I9224756 |
| authorships[0].affiliations[0].raw_affiliation_string | College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, People's Republic of China |
| authorships[0].institutions[0].id | https://openalex.org/I9224756 |
| authorships[0].institutions[0].ror | https://ror.org/03awzbc87 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I9224756 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Northeastern University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shuhuan Wang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, People's Republic of China |
| authorships[1].author.id | https://openalex.org/A5100736053 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5629-3816 |
| authorships[1].author.display_name | Zhiqing Li |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I9224756 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, People's Republic of China |
| authorships[1].institutions[0].id | https://openalex.org/I9224756 |
| authorships[1].institutions[0].ror | https://ror.org/03awzbc87 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I9224756 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Northeastern University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhiqing Li |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, People's Republic of China |
| authorships[2].author.id | https://openalex.org/A5073859796 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8763-1582 |
| authorships[2].author.display_name | Lingmin Liao |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I141649914, https://openalex.org/I4210108480 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, People's Republic of China |
| authorships[2].institutions[0].id | https://openalex.org/I141649914 |
| authorships[2].institutions[0].ror | https://ror.org/042v6xz23 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I141649914 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Nanchang University |
| authorships[2].institutions[1].id | https://openalex.org/I4210108480 |
| authorships[2].institutions[1].ror | https://ror.org/01nxv5c88 |
| authorships[2].institutions[1].type | healthcare |
| authorships[2].institutions[1].lineage | https://openalex.org/I4210108480 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Second Affiliated Hospital of Nanchang University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Lingmin Liao |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, People's Republic of China |
| authorships[3].author.id | https://openalex.org/A5059607621 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4709-1603 |
| authorships[3].author.display_name | Chunquan Zhang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I141649914, https://openalex.org/I4210108480 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, People's Republic of China |
| authorships[3].institutions[0].id | https://openalex.org/I141649914 |
| authorships[3].institutions[0].ror | https://ror.org/042v6xz23 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I141649914 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Nanchang University |
| authorships[3].institutions[1].id | https://openalex.org/I4210108480 |
| authorships[3].institutions[1].ror | https://ror.org/01nxv5c88 |
| authorships[3].institutions[1].type | healthcare |
| authorships[3].institutions[1].lineage | https://openalex.org/I4210108480 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Second Affiliated Hospital of Nanchang University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Chunquan Zhang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, People's Republic of China |
| authorships[4].author.id | https://openalex.org/A5103027715 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-7559-3782 |
| authorships[4].author.display_name | Jiali Zhao |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I141649914, https://openalex.org/I4210108480 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, People's Republic of China |
| authorships[4].institutions[0].id | https://openalex.org/I141649914 |
| authorships[4].institutions[0].ror | https://ror.org/042v6xz23 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I141649914 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Nanchang University |
| authorships[4].institutions[1].id | https://openalex.org/I4210108480 |
| authorships[4].institutions[1].ror | https://ror.org/01nxv5c88 |
| authorships[4].institutions[1].type | healthcare |
| authorships[4].institutions[1].lineage | https://openalex.org/I4210108480 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | Second Affiliated Hospital of Nanchang University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Jiali Zhao |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, People's Republic of China |
| authorships[5].author.id | https://openalex.org/A5103233383 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-9331-0985 |
| authorships[5].author.display_name | Liang Sang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210140515, https://openalex.org/I91656880 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Ultrasound, The First Hospital of China Medical University, Shenyang, People's Republic of China |
| authorships[5].institutions[0].id | https://openalex.org/I91656880 |
| authorships[5].institutions[0].ror | https://ror.org/032d4f246 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I91656880 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | China Medical University |
| authorships[5].institutions[1].id | https://openalex.org/I4210140515 |
| authorships[5].institutions[1].ror | https://ror.org/04wjghj95 |
| authorships[5].institutions[1].type | healthcare |
| authorships[5].institutions[1].lineage | https://openalex.org/I4210140515 |
| authorships[5].institutions[1].country_code | CN |
| authorships[5].institutions[1].display_name | First Hospital of China Medical University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Liang Sang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Ultrasound, The First Hospital of China Medical University, Shenyang, People's Republic of China |
| authorships[6].author.id | https://openalex.org/A5100374757 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-9563-721X |
| authorships[6].author.display_name | Wei Qian |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I9224756 |
| authorships[6].affiliations[0].raw_affiliation_string | College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, People's Republic of China |
| authorships[6].institutions[0].id | https://openalex.org/I9224756 |
| authorships[6].institutions[0].ror | https://ror.org/03awzbc87 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I9224756 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Northeastern University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Wei Qian |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, People's Republic of China |
| authorships[7].author.id | https://openalex.org/A5078380098 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-9645-8558 |
| authorships[7].author.display_name | Guangyao Pan |
| authorships[7].countries | CN, SG |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I165932596, https://openalex.org/I4210125878 |
| authorships[7].affiliations[0].raw_affiliation_string | National University of Singapore (Suzhou) Research Institute, Suzhou, Jiangsu, 215123, People's Republic of China |
| authorships[7].institutions[0].id | https://openalex.org/I4210125878 |
| authorships[7].institutions[0].ror | https://ror.org/03ebk0c60 |
| authorships[7].institutions[0].type | facility |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210125878 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Suzhou Research Institute |
| authorships[7].institutions[1].id | https://openalex.org/I165932596 |
| authorships[7].institutions[1].ror | https://ror.org/01tgyzw49 |
| authorships[7].institutions[1].type | education |
| authorships[7].institutions[1].lineage | https://openalex.org/I165932596 |
| authorships[7].institutions[1].country_code | SG |
| authorships[7].institutions[1].display_name | National University of Singapore |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | GuangYao Pan |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | National University of Singapore (Suzhou) Research Institute, Suzhou, Jiangsu, 215123, People's Republic of China |
| authorships[8].author.id | https://openalex.org/A5015416321 |
| authorships[8].author.orcid | https://orcid.org/0000-0001-5877-7136 |
| authorships[8].author.display_name | Long Huang |
| authorships[8].countries | CN |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I141649914, https://openalex.org/I4210108480 |
| authorships[8].affiliations[0].raw_affiliation_string | Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, People's Republic of China |
| authorships[8].institutions[0].id | https://openalex.org/I141649914 |
| authorships[8].institutions[0].ror | https://ror.org/042v6xz23 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I141649914 |
| authorships[8].institutions[0].country_code | CN |
| authorships[8].institutions[0].display_name | Nanchang University |
| authorships[8].institutions[1].id | https://openalex.org/I4210108480 |
| authorships[8].institutions[1].ror | https://ror.org/01nxv5c88 |
| authorships[8].institutions[1].type | healthcare |
| authorships[8].institutions[1].lineage | https://openalex.org/I4210108480 |
| authorships[8].institutions[1].country_code | CN |
| authorships[8].institutions[1].display_name | Second Affiliated Hospital of Nanchang University |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Long Huang |
| authorships[8].is_corresponding | True |
| authorships[8].raw_affiliation_strings | Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, People's Republic of China |
| authorships[9].author.id | https://openalex.org/A5069485824 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-5054-3586 |
| authorships[9].author.display_name | He Ma |
| authorships[9].countries | CN, SG |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I165932596, https://openalex.org/I4210125878 |
| authorships[9].affiliations[0].raw_affiliation_string | National University of Singapore (Suzhou) Research Institute, Suzhou, Jiangsu, 215123, People's Republic of China |
| authorships[9].affiliations[1].institution_ids | https://openalex.org/I9224756 |
| authorships[9].affiliations[1].raw_affiliation_string | College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, People's Republic of China |
| authorships[9].institutions[0].id | https://openalex.org/I9224756 |
| authorships[9].institutions[0].ror | https://ror.org/03awzbc87 |
| authorships[9].institutions[0].type | education |
| authorships[9].institutions[0].lineage | https://openalex.org/I9224756 |
| authorships[9].institutions[0].country_code | CN |
| authorships[9].institutions[0].display_name | Northeastern University |
| authorships[9].institutions[1].id | https://openalex.org/I4210125878 |
| authorships[9].institutions[1].ror | https://ror.org/03ebk0c60 |
| authorships[9].institutions[1].type | facility |
| authorships[9].institutions[1].lineage | https://openalex.org/I4210125878 |
| authorships[9].institutions[1].country_code | CN |
| authorships[9].institutions[1].display_name | Suzhou Research Institute |
| authorships[9].institutions[2].id | https://openalex.org/I165932596 |
| authorships[9].institutions[2].ror | https://ror.org/01tgyzw49 |
| authorships[9].institutions[2].type | education |
| authorships[9].institutions[2].lineage | https://openalex.org/I165932596 |
| authorships[9].institutions[2].country_code | SG |
| authorships[9].institutions[2].display_name | National University of Singapore |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | He Ma |
| authorships[9].is_corresponding | True |
| authorships[9].raw_affiliation_strings | College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110169, People's Republic of China, National University of Singapore (Suzhou) Research Institute, Suzhou, Jiangsu, 215123, People's Republic of China |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://iopscience.iop.org/article/10.1088/1361-6560/ace6f1/pdf |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | DPAM-PSPNet: ultrasonic image segmentation of thyroid nodule based on dual-path attention mechanism |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12422 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9889000058174133 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Radiomics and Machine Learning in Medical Imaging |
| related_works | https://openalex.org/W3029276719, https://openalex.org/W3125611560, https://openalex.org/W2498632914, https://openalex.org/W45411538, https://openalex.org/W59410728, https://openalex.org/W2525654528, https://openalex.org/W3031210199, https://openalex.org/W2092893319, https://openalex.org/W3031740696, https://openalex.org/W2333230371 |
| cited_by_count | 12 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 8 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1088/1361-6560/ace6f1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S20241394 |
| best_oa_location.source.issn | 0031-9155, 1361-6560 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0031-9155 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Physics in Medicine and Biology |
| best_oa_location.source.host_organization | https://openalex.org/P4310320083 |
| best_oa_location.source.host_organization_name | IOP Publishing |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| best_oa_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://iopscience.iop.org/article/10.1088/1361-6560/ace6f1/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Physics in Medicine & Biology |
| best_oa_location.landing_page_url | https://doi.org/10.1088/1361-6560/ace6f1 |
| primary_location.id | doi:10.1088/1361-6560/ace6f1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S20241394 |
| primary_location.source.issn | 0031-9155, 1361-6560 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0031-9155 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Physics in Medicine and Biology |
| primary_location.source.host_organization | https://openalex.org/P4310320083 |
| primary_location.source.host_organization_name | IOP Publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| primary_location.license | |
| primary_location.pdf_url | https://iopscience.iop.org/article/10.1088/1361-6560/ace6f1/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Physics in Medicine & Biology |
| primary_location.landing_page_url | https://doi.org/10.1088/1361-6560/ace6f1 |
| publication_date | 2023-07-12 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W4309762183, https://openalex.org/W2963881378, https://openalex.org/W4317435744, https://openalex.org/W6795435739, https://openalex.org/W4221095777, https://openalex.org/W2983943839, https://openalex.org/W4313530887, https://openalex.org/W6747836079, https://openalex.org/W6790275670, https://openalex.org/W6748481559, https://openalex.org/W6772921803, https://openalex.org/W3013198566, https://openalex.org/W6795974180, https://openalex.org/W4312084891, https://openalex.org/W3088257970, https://openalex.org/W2109255472, https://openalex.org/W6737664043, https://openalex.org/W6743731764, https://openalex.org/W4296034223, https://openalex.org/W4220818163, https://openalex.org/W6773246927, https://openalex.org/W3034328552, https://openalex.org/W6638444622, https://openalex.org/W6742348326, https://openalex.org/W4306630545, https://openalex.org/W6640054144, https://openalex.org/W4214561696, https://openalex.org/W6729021199, https://openalex.org/W6750469568, https://openalex.org/W6795591883, https://openalex.org/W6657413401, https://openalex.org/W2089630167, https://openalex.org/W1901129140, https://openalex.org/W6690495727, https://openalex.org/W6773705768, https://openalex.org/W6768952390, https://openalex.org/W2133665775, https://openalex.org/W6753412334, https://openalex.org/W3109470666, https://openalex.org/W6734721309, https://openalex.org/W6801786522, https://openalex.org/W2788906943, https://openalex.org/W6730342312, https://openalex.org/W3004662472, https://openalex.org/W3006075760, https://openalex.org/W3165252795, https://openalex.org/W3203942451, https://openalex.org/W2884561390, https://openalex.org/W2798122215, https://openalex.org/W2964309882, https://openalex.org/W2594069063, https://openalex.org/W2884585870, https://openalex.org/W4294226146, https://openalex.org/W2544543335, https://openalex.org/W2395611524, https://openalex.org/W3127751679, https://openalex.org/W4297775537, https://openalex.org/W3165326420, https://openalex.org/W2027685755, https://openalex.org/W2783380535, https://openalex.org/W2560023338, https://openalex.org/W3160284783, https://openalex.org/W3034552520, https://openalex.org/W2243073304, https://openalex.org/W2963420686, https://openalex.org/W3096812112, https://openalex.org/W3002393520 |
| referenced_works_count | 67 |
| abstract_inverted_index.a | 66, 116 |
| abstract_inverted_index.In | 63, 88, 108, 121, 196 |
| abstract_inverted_index.We | 137 |
| abstract_inverted_index.an | 53, 205 |
| abstract_inverted_index.by | 98 |
| abstract_inverted_index.in | 8, 13, 76, 106, 192, 228 |
| abstract_inverted_index.is | 48, 61, 96 |
| abstract_inverted_index.it | 111, 124, 201 |
| abstract_inverted_index.of | 22, 28, 93, 207, 210, 213, 218 |
| abstract_inverted_index.on | 39, 126, 178 |
| abstract_inverted_index.to | 33, 82, 144 |
| abstract_inverted_index.Our | 182 |
| abstract_inverted_index.The | 25, 150, 221 |
| abstract_inverted_index.and | 19, 43, 50, 80, 129, 168, 173, 215, 235 |
| abstract_inverted_index.can | 224 |
| abstract_inverted_index.for | 56, 163, 240 |
| abstract_inverted_index.has | 4 |
| abstract_inverted_index.its | 6 |
| abstract_inverted_index.mPA | 209 |
| abstract_inverted_index.new | 170 |
| abstract_inverted_index.one | 109 |
| abstract_inverted_index.the | 9, 100, 133, 140, 146, 155, 164, 169, 179, 197 |
| abstract_inverted_index.was | 70, 152 |
| abstract_inverted_index.Deep | 2 |
| abstract_inverted_index.Dice | 216 |
| abstract_inverted_index.Main | 148 |
| abstract_inverted_index.This | 46 |
| abstract_inverted_index.also | 138 |
| abstract_inverted_index.data | 234 |
| abstract_inverted_index.dual | 101 |
| abstract_inverted_index.loss | 142, 171 |
| abstract_inverted_index.mIOU | 206 |
| abstract_inverted_index.path | 102 |
| abstract_inverted_index.task | 47 |
| abstract_inverted_index.that | 186 |
| abstract_inverted_index.this | 64, 89 |
| abstract_inverted_index.were | 161, 176 |
| abstract_inverted_index.with | 115, 204, 231 |
| abstract_inverted_index.based | 38 |
| abstract_inverted_index.edges | 95 |
| abstract_inverted_index.focus | 125 |
| abstract_inverted_index.forms | 21 |
| abstract_inverted_index.image | 15, 17 |
| abstract_inverted_index.model | 67, 198, 223 |
| abstract_inverted_index.named | 68 |
| abstract_inverted_index.nodal | 127 |
| abstract_inverted_index.other | 20, 122, 189 |
| abstract_inverted_index.their | 44 |
| abstract_inverted_index.these | 241 |
| abstract_inverted_index.which | 72 |
| abstract_inverted_index.(DPAM) | 105 |
| abstract_inverted_index.bridge | 135 |
| abstract_inverted_index.field, | 11 |
| abstract_inverted_index.global | 113 |
| abstract_inverted_index.images | 79, 230 |
| abstract_inverted_index.little | 232 |
| abstract_inverted_index.locate | 34 |
| abstract_inverted_index.model. | 158 |
| abstract_inverted_index.nodule | 40, 59, 94 |
| abstract_inverted_index.paper, | 90 |
| abstract_inverted_index.public | 180 |
| abstract_inverted_index.study, | 65 |
| abstract_inverted_index.system | 55 |
| abstract_inverted_index.tested | 153 |
| abstract_inverted_index.0.8675, | 208 |
| abstract_inverted_index.0.9202, | 214 |
| abstract_inverted_index.0.9213. | 219 |
| abstract_inverted_index.0.9357, | 211 |
| abstract_inverted_index.PSPNet. | 107 |
| abstract_inverted_index.against | 154 |
| abstract_inverted_index.enables | 81 |
| abstract_inverted_index.margins | 128 |
| abstract_inverted_index.medical | 10, 14 |
| abstract_inverted_index.methods | 191 |
| abstract_inverted_index.nodules | 30, 75, 85, 227 |
| abstract_inverted_index.regions | 239 |
| abstract_inverted_index.results | 184 |
| abstract_inverted_index.segment | 83, 225 |
| abstract_inverted_index.through | 132 |
| abstract_inverted_index.thyroid | 29, 58, 77, 226 |
| abstract_inverted_index.tiring; | 51 |
| abstract_inverted_index.updated | 139 |
| abstract_inverted_index.various | 193 |
| abstract_inverted_index.Ablation | 159 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accurate | 57, 91, 237 |
| abstract_inverted_index.achieved | 97, 202 |
| abstract_inverted_index.boundary | 238 |
| abstract_inverted_index.captures | 112 |
| abstract_inverted_index.channel, | 110, 123 |
| abstract_inverted_index.clinical | 26 |
| abstract_inverted_index.dataset. | 181 |
| abstract_inverted_index.designed | 162, 177 |
| abstract_inverted_index.diagnose | 36 |
| abstract_inverted_index.existing | 190 |
| abstract_inverted_index.function | 143 |
| abstract_inverted_index.generate | 236 |
| abstract_inverted_index.learning | 3 |
| abstract_inverted_index.metrics. | 195 |
| abstract_inverted_index.network. | 136 |
| abstract_inverted_index.nodules, | 35 |
| abstract_inverted_index.nodules. | 242 |
| abstract_inverted_index.requires | 31 |
| abstract_inverted_index.residual | 134 |
| abstract_inverted_index.results. | 149 |
| abstract_inverted_index.segments | 74 |
| abstract_inverted_index.textures | 42 |
| abstract_inverted_index.training | 233 |
| abstract_inverted_index.two-path | 165 |
| abstract_inverted_index.Approach. | 87 |
| abstract_inverted_index.attention | 103, 166 |
| abstract_inverted_index.automated | 23, 54 |
| abstract_inverted_index.classical | 156 |
| abstract_inverted_index.diagnosis | 27 |
| abstract_inverted_index.function, | 172 |
| abstract_inverted_index.malignant | 84 |
| abstract_inverted_index.mechanism | 104, 167 |
| abstract_inverted_index.proposed, | 71 |
| abstract_inverted_index.Objective. | 1 |
| abstract_inverted_index.comparison | 199 |
| abstract_inverted_index.conditions | 37 |
| abstract_inverted_index.essential. | 62 |
| abstract_inverted_index.evaluation | 194 |
| abstract_inverted_index.integrated | 141 |
| abstract_inverted_index.mPrecision | 212 |
| abstract_inverted_index.mechanism. | 120 |
| abstract_inverted_index.precisely. | 86 |
| abstract_inverted_index.therefore, | 52 |
| abstract_inverted_index.ultrasound | 78, 229 |
| abstract_inverted_index.DPAM-PSPNet | 69, 151, 187, 222 |
| abstract_inverted_index.accommodate | 145 |
| abstract_inverted_index.boundaries, | 41 |
| abstract_inverted_index.coefficient | 217 |
| abstract_inverted_index.demonstrate | 185 |
| abstract_inverted_index.experience. | 45 |
| abstract_inverted_index.experiments | 160, 175 |
| abstract_inverted_index.information | 114, 131 |
| abstract_inverted_index.interaction | 119 |
| abstract_inverted_index.introducing | 99 |
| abstract_inverted_index.lightweight | 117 |
| abstract_inverted_index.outperforms | 188 |
| abstract_inverted_index.performance | 203 |
| abstract_inverted_index.surrounding | 130 |
| abstract_inverted_index.versatility | 7 |
| abstract_inverted_index.DPAM-PSPNet. | 147 |
| abstract_inverted_index.demonstrated | 5 |
| abstract_inverted_index.diagnostics. | 24 |
| abstract_inverted_index.experimental | 183 |
| abstract_inverted_index.experiments, | 200 |
| abstract_inverted_index.particularly | 12 |
| abstract_inverted_index.radiologists | 32 |
| abstract_inverted_index.segmentation | 60, 92, 157 |
| abstract_inverted_index.Significance. | 220 |
| abstract_inverted_index.automatically | 73 |
| abstract_inverted_index.cross-channel | 118 |
| abstract_inverted_index.segmentation, | 16 |
| abstract_inverted_index.generalization | 174 |
| abstract_inverted_index.classification, | 18 |
| abstract_inverted_index.labor-intensive | 49 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5069485824, https://openalex.org/A5015416321 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I141649914, https://openalex.org/I165932596, https://openalex.org/I4210108480, https://openalex.org/I4210125878, https://openalex.org/I9224756 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.92052273 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |