Deep‐learning model observer for a low‐contrast hepatic metastases localization task in computed tomography Article Swipe
Hao Gong
,
Joel G. Fletcher
,
Jay P. Heiken
,
Michael Wells
,
Shuai Leng
,
Cynthia H. McCollough
,
Lifeng Yu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1002/mp.15362
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1002/mp.15362
Purpose Conventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict human observer performance in clinical CT tasks that involve realistic anatomical background. Deep‐learning‐based model observers (DL‐MO) have recently been developed, but have not been validated for challenging low contrast diagnostic tasks in abdominal CT. We consequently sought to validate a DL‐MO for a low‐contrast hepatic metastases localization task. Methods We adapted our recently developed DL‐MO framework for the liver metastases localization task. Our previously‐validated projection‐domain lesion‐/noise‐insertion techniques were used to synthesize realistic positive and low‐dose abdominal CT exams, using the archived patient projection data. Ten experimental conditions were generated, which involved different lesion sizes/contrasts, radiation dose levels, and image reconstruction types. Each condition included 100 trials generated from a patient cohort of 7 cases. Each trial was presented as liver image patches (160×160×5 voxels). The DL‐MO performance was calculated for each condition and was compared with human observer performance, which was obtained by three sub‐specialized radiologists in an observer study. The performance of DL‐MO and radiologists was gauged by the area under localization receiver‐operating‐characteristic curves. The generalization performance of the DL‐MO was estimated with the repeated twofold cross‐validation method over the same set of trials used in the human observer study. A multi‐slice Channelized Hoteling Observers (CHO) was compared with the DL‐MO across the same experimental conditions. Results The performance of DL‐MO was highly correlated to that of radiologists (Pearson's correlation coefficient: 0.987; 95% CI: [0.942, 0.997]). The performance level of DL‐MO was comparable to that of the grouped radiologists, that is, the mean performance difference was ‐3.3%. The CHO performance was poorer than the grouped radiologist performance, before internal noise could be added. The correlation between CHO and radiologists was weaker (Pearson's correlation coefficient: 0.812, and 95% CI: [0.378, 0.955]), and the corresponding performance bias (‐29.5%) was statistically significant. Conclusion The presented study demonstrated the potential of using the DL‐MO for image quality assessment in patient abdominal CT tasks.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/mp.15362
- OA Status
- green
- Cited By
- 14
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3213557328
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3213557328Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/mp.15362Digital Object Identifier
- Title
-
Deep‐learning model observer for a low‐contrast hepatic metastases localization task in computed tomographyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-18Full publication date if available
- Authors
-
Hao Gong, Joel G. Fletcher, Jay P. Heiken, Michael Wells, Shuai Leng, Cynthia H. McCollough, Lifeng YuList of authors in order
- Landing page
-
https://doi.org/10.1002/mp.15362Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/8758536Direct OA link when available
- Concepts
-
Voxel, Artificial intelligence, Observer (physics), Contrast (vision), Generalizability theory, Medical imaging, Computer science, Medicine, Nuclear medicine, Iterative reconstruction, Radiology, Pattern recognition (psychology), Computer vision, Mathematics, Statistics, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 5, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3213557328 |
|---|---|
| doi | https://doi.org/10.1002/mp.15362 |
| ids.doi | https://doi.org/10.1002/mp.15362 |
| ids.mag | 3213557328 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/34792800 |
| ids.openalex | https://openalex.org/W3213557328 |
| fwci | 1.85595011 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000465 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Algorithms |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D000077321 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Deep Learning |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D006801 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Humans |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D007091 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Image Processing, Computer-Assisted |
| mesh[4].qualifier_ui | Q000000981 |
| mesh[4].descriptor_ui | D008113 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | diagnostic imaging |
| mesh[4].descriptor_name | Liver Neoplasms |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D015588 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Observer Variation |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D019047 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Phantoms, Imaging |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D011829 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Radiation Dosage |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D014057 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Tomography, X-Ray Computed |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D000465 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Algorithms |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D000077321 |
| mesh[10].is_major_topic | True |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Deep Learning |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D006801 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Humans |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D007091 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Image Processing, Computer-Assisted |
| mesh[13].qualifier_ui | Q000000981 |
| mesh[13].descriptor_ui | D008113 |
| mesh[13].is_major_topic | True |
| mesh[13].qualifier_name | diagnostic imaging |
| mesh[13].descriptor_name | Liver Neoplasms |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D015588 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Observer Variation |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D019047 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Phantoms, Imaging |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D011829 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Radiation Dosage |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D014057 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Tomography, X-Ray Computed |
| type | article |
| title | Deep‐learning model observer for a low‐contrast hepatic metastases localization task in computed tomography |
| biblio.issue | 1 |
| biblio.volume | 49 |
| biblio.last_page | 83 |
| biblio.first_page | 70 |
| topics[0].id | https://openalex.org/T10522 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9994999766349792 |
| 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 | Medical Imaging Techniques and Applications |
| topics[1].id | https://openalex.org/T10844 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9994000196456909 |
| 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 | Radiation Dose and Imaging |
| topics[2].id | https://openalex.org/T11894 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9987999796867371 |
| 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 | Radiology practices and education |
| is_xpac | False |
| apc_list.value | 3040 |
| apc_list.currency | USD |
| apc_list.value_usd | 3040 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C54170458 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6655400395393372 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q663554 |
| concepts[0].display_name | Voxel |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6109391450881958 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C2780704645 |
| concepts[2].level | 2 |
| concepts[2].score | 0.609738826751709 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q9251458 |
| concepts[2].display_name | Observer (physics) |
| concepts[3].id | https://openalex.org/C2776502983 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5450735688209534 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q690182 |
| concepts[3].display_name | Contrast (vision) |
| concepts[4].id | https://openalex.org/C27158222 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5181423425674438 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5532422 |
| concepts[4].display_name | Generalizability theory |
| concepts[5].id | https://openalex.org/C31601959 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5004703998565674 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q931309 |
| concepts[5].display_name | Medical imaging |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.4566667377948761 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C71924100 |
| concepts[7].level | 0 |
| concepts[7].score | 0.4278967082500458 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[7].display_name | Medicine |
| concepts[8].id | https://openalex.org/C2989005 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4155625104904175 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q214963 |
| concepts[8].display_name | Nuclear medicine |
| concepts[9].id | https://openalex.org/C141379421 |
| concepts[9].level | 2 |
| concepts[9].score | 0.41453588008880615 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q6094427 |
| concepts[9].display_name | Iterative reconstruction |
| concepts[10].id | https://openalex.org/C126838900 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3780399262905121 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[10].display_name | Radiology |
| concepts[11].id | https://openalex.org/C153180895 |
| concepts[11].level | 2 |
| concepts[11].score | 0.3753564953804016 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[11].display_name | Pattern recognition (psychology) |
| concepts[12].id | https://openalex.org/C31972630 |
| concepts[12].level | 1 |
| concepts[12].score | 0.3467806577682495 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[12].display_name | Computer vision |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.2686156630516052 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C105795698 |
| concepts[14].level | 1 |
| concepts[14].score | 0.09405088424682617 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[14].display_name | Statistics |
| concepts[15].id | https://openalex.org/C62520636 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[15].display_name | Quantum mechanics |
| concepts[16].id | https://openalex.org/C121332964 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[16].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/voxel |
| keywords[0].score | 0.6655400395393372 |
| keywords[0].display_name | Voxel |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.6109391450881958 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/observer |
| keywords[2].score | 0.609738826751709 |
| keywords[2].display_name | Observer (physics) |
| keywords[3].id | https://openalex.org/keywords/contrast |
| keywords[3].score | 0.5450735688209534 |
| keywords[3].display_name | Contrast (vision) |
| keywords[4].id | https://openalex.org/keywords/generalizability-theory |
| keywords[4].score | 0.5181423425674438 |
| keywords[4].display_name | Generalizability theory |
| keywords[5].id | https://openalex.org/keywords/medical-imaging |
| keywords[5].score | 0.5004703998565674 |
| keywords[5].display_name | Medical imaging |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.4566667377948761 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/medicine |
| keywords[7].score | 0.4278967082500458 |
| keywords[7].display_name | Medicine |
| keywords[8].id | https://openalex.org/keywords/nuclear-medicine |
| keywords[8].score | 0.4155625104904175 |
| keywords[8].display_name | Nuclear medicine |
| keywords[9].id | https://openalex.org/keywords/iterative-reconstruction |
| keywords[9].score | 0.41453588008880615 |
| keywords[9].display_name | Iterative reconstruction |
| keywords[10].id | https://openalex.org/keywords/radiology |
| keywords[10].score | 0.3780399262905121 |
| keywords[10].display_name | Radiology |
| keywords[11].id | https://openalex.org/keywords/pattern-recognition |
| keywords[11].score | 0.3753564953804016 |
| keywords[11].display_name | Pattern recognition (psychology) |
| keywords[12].id | https://openalex.org/keywords/computer-vision |
| keywords[12].score | 0.3467806577682495 |
| keywords[12].display_name | Computer vision |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.2686156630516052 |
| keywords[13].display_name | Mathematics |
| keywords[14].id | https://openalex.org/keywords/statistics |
| keywords[14].score | 0.09405088424682617 |
| keywords[14].display_name | Statistics |
| language | en |
| locations[0].id | doi:10.1002/mp.15362 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S95522064 |
| locations[0].source.issn | 0094-2405, 1522-8541, 2473-4209 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0094-2405 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Medical Physics |
| locations[0].source.host_organization | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_name | Wiley |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_lineage_names | Wiley |
| locations[0].license | |
| locations[0].pdf_url | |
| 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 | Medical Physics |
| locations[0].landing_page_url | https://doi.org/10.1002/mp.15362 |
| locations[1].id | pmid:34792800 |
| 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 | Medical physics |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/34792800 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:8758536 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Med Phys |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8758536 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5033648312 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1123-7172 |
| authorships[0].author.display_name | Hao Gong |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I1330342723 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[0].institutions[0].id | https://openalex.org/I1330342723 |
| authorships[0].institutions[0].ror | https://ror.org/02qp3tb03 |
| authorships[0].institutions[0].type | healthcare |
| authorships[0].institutions[0].lineage | https://openalex.org/I1330342723 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Mayo Clinic |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hao Gong |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[1].author.id | https://openalex.org/A5032222962 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8941-5434 |
| authorships[1].author.display_name | Joel G. Fletcher |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1330342723 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[1].institutions[0].id | https://openalex.org/I1330342723 |
| authorships[1].institutions[0].ror | https://ror.org/02qp3tb03 |
| authorships[1].institutions[0].type | healthcare |
| authorships[1].institutions[0].lineage | https://openalex.org/I1330342723 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Mayo Clinic |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Joel G. Fletcher |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[2].author.id | https://openalex.org/A5060852973 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4488-2888 |
| authorships[2].author.display_name | Jay P. Heiken |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1330342723 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[2].institutions[0].id | https://openalex.org/I1330342723 |
| authorships[2].institutions[0].ror | https://ror.org/02qp3tb03 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I1330342723 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Mayo Clinic |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jay P. Heiken |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[3].author.id | https://openalex.org/A5048740187 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9411-6087 |
| authorships[3].author.display_name | Michael Wells |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I1330342723 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[3].institutions[0].id | https://openalex.org/I1330342723 |
| authorships[3].institutions[0].ror | https://ror.org/02qp3tb03 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I1330342723 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Mayo Clinic |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Michael L. Wells |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[4].author.id | https://openalex.org/A5051084260 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6453-9481 |
| authorships[4].author.display_name | Shuai Leng |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1330342723 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[4].institutions[0].id | https://openalex.org/I1330342723 |
| authorships[4].institutions[0].ror | https://ror.org/02qp3tb03 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I1330342723 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Mayo Clinic |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Shuai Leng |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[5].author.id | https://openalex.org/A5067668310 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5346-332X |
| authorships[5].author.display_name | Cynthia H. McCollough |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I1330342723 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[5].institutions[0].id | https://openalex.org/I1330342723 |
| authorships[5].institutions[0].ror | https://ror.org/02qp3tb03 |
| authorships[5].institutions[0].type | healthcare |
| authorships[5].institutions[0].lineage | https://openalex.org/I1330342723 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | Mayo Clinic |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Cynthia H. McCollough |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[6].author.id | https://openalex.org/A5047914292 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-4267-1326 |
| authorships[6].author.display_name | Lifeng Yu |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I1330342723 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| authorships[6].institutions[0].id | https://openalex.org/I1330342723 |
| authorships[6].institutions[0].ror | https://ror.org/02qp3tb03 |
| authorships[6].institutions[0].type | healthcare |
| authorships[6].institutions[0].lineage | https://openalex.org/I1330342723 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | Mayo Clinic |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Lifeng Yu |
| authorships[6].is_corresponding | True |
| authorships[6].raw_affiliation_strings | Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8758536 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Deep‐learning model observer for a low‐contrast hepatic metastases localization task in computed tomography |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10522 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9994999766349792 |
| 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 | Medical Imaging Techniques and Applications |
| related_works | https://openalex.org/W2118717649, https://openalex.org/W410723623, https://openalex.org/W2413243053, https://openalex.org/W2015341305, https://openalex.org/W4225593417, https://openalex.org/W2035068594, https://openalex.org/W2573498121, https://openalex.org/W3022298670, https://openalex.org/W3160494304, https://openalex.org/W1549477351 |
| cited_by_count | 14 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 5 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 5 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:8758536 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Med Phys |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8758536 |
| primary_location.id | doi:10.1002/mp.15362 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S95522064 |
| primary_location.source.issn | 0094-2405, 1522-8541, 2473-4209 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0094-2405 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Medical Physics |
| primary_location.source.host_organization | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_name | Wiley |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_lineage_names | Wiley |
| primary_location.license | |
| primary_location.pdf_url | |
| 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 | Medical Physics |
| primary_location.landing_page_url | https://doi.org/10.1002/mp.15362 |
| publication_date | 2021-11-18 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2004749492, https://openalex.org/W2122546849, https://openalex.org/W2591795972, https://openalex.org/W2590236471, https://openalex.org/W2195096812, https://openalex.org/W2138895317, https://openalex.org/W2029788061, https://openalex.org/W2099078477, https://openalex.org/W2618141385, https://openalex.org/W2129940523, https://openalex.org/W2068863313, https://openalex.org/W2938269440, https://openalex.org/W2939018358, https://openalex.org/W1973884420, https://openalex.org/W2077195075, https://openalex.org/W2161190560, https://openalex.org/W2592066832, https://openalex.org/W3012358003, https://openalex.org/W2123374810, https://openalex.org/W2045784801, https://openalex.org/W2071474104, https://openalex.org/W1979723430, https://openalex.org/W2793088928, https://openalex.org/W2918658282, https://openalex.org/W2592525318, https://openalex.org/W2946247589, https://openalex.org/W3010730313, https://openalex.org/W3012185047, https://openalex.org/W2888365089, https://openalex.org/W3081758339, https://openalex.org/W2924878444, https://openalex.org/W3039633865, https://openalex.org/W2918522658, https://openalex.org/W2007241489, https://openalex.org/W2178756635, https://openalex.org/W2334619767, https://openalex.org/W2319126251, https://openalex.org/W2194775991, https://openalex.org/W2108598243, https://openalex.org/W1971724710, https://openalex.org/W2071470851, https://openalex.org/W2167676007, https://openalex.org/W2954281887, https://openalex.org/W2915843931, https://openalex.org/W2168199261, https://openalex.org/W2122629586, https://openalex.org/W2089344753, https://openalex.org/W2050351309, https://openalex.org/W2160022743, https://openalex.org/W2062006303, https://openalex.org/W1582684391, https://openalex.org/W2135699773, https://openalex.org/W2141007997, https://openalex.org/W2099461322, https://openalex.org/W809667723, https://openalex.org/W4214627933, https://openalex.org/W2593305280, https://openalex.org/W3011114009, https://openalex.org/W3100045265 |
| referenced_works_count | 59 |
| abstract_inverted_index.7 | 154 |
| abstract_inverted_index.A | 233 |
| abstract_inverted_index.a | 12, 81, 84, 150 |
| abstract_inverted_index.CT | 7, 47, 118, 350 |
| abstract_inverted_index.It | 29 |
| abstract_inverted_index.We | 76, 91 |
| abstract_inverted_index.an | 26, 189 |
| abstract_inverted_index.as | 160 |
| abstract_inverted_index.be | 23, 37, 304 |
| abstract_inverted_index.by | 184, 200 |
| abstract_inverted_index.if | 32 |
| abstract_inverted_index.in | 6, 25, 45, 73, 188, 228, 347 |
| abstract_inverted_index.is | 19, 30 |
| abstract_inverted_index.of | 153, 194, 210, 225, 252, 259, 272, 278, 339 |
| abstract_inverted_index.or | 15 |
| abstract_inverted_index.to | 11, 40, 79, 111, 257, 276 |
| abstract_inverted_index.100 | 146 |
| abstract_inverted_index.95% | 265, 319 |
| abstract_inverted_index.CHO | 291, 309 |
| abstract_inverted_index.CI: | 266, 320 |
| abstract_inverted_index.CT. | 75 |
| abstract_inverted_index.MOs | 35 |
| abstract_inverted_index.Our | 104 |
| abstract_inverted_index.Ten | 126 |
| abstract_inverted_index.The | 166, 192, 207, 250, 269, 290, 306, 333 |
| abstract_inverted_index.and | 21, 115, 139, 174, 196, 310, 318, 323 |
| abstract_inverted_index.are | 8 |
| abstract_inverted_index.but | 62 |
| abstract_inverted_index.can | 22, 36 |
| abstract_inverted_index.for | 67, 83, 98, 171, 343 |
| abstract_inverted_index.is, | 283 |
| abstract_inverted_index.low | 69 |
| abstract_inverted_index.not | 64 |
| abstract_inverted_index.our | 93 |
| abstract_inverted_index.set | 224 |
| abstract_inverted_index.the | 99, 121, 201, 211, 216, 222, 229, 242, 245, 279, 284, 296, 324, 337, 341 |
| abstract_inverted_index.was | 158, 169, 175, 182, 198, 213, 239, 254, 274, 288, 293, 312, 329 |
| abstract_inverted_index.(MO) | 5 |
| abstract_inverted_index.Each | 143, 156 |
| abstract_inverted_index.area | 202 |
| abstract_inverted_index.been | 60, 65 |
| abstract_inverted_index.bias | 327 |
| abstract_inverted_index.dose | 137 |
| abstract_inverted_index.each | 172 |
| abstract_inverted_index.from | 149 |
| abstract_inverted_index.have | 58, 63 |
| abstract_inverted_index.mean | 285 |
| abstract_inverted_index.over | 221 |
| abstract_inverted_index.same | 223, 246 |
| abstract_inverted_index.than | 295 |
| abstract_inverted_index.that | 18, 49, 258, 277, 282 |
| abstract_inverted_index.used | 110, 227 |
| abstract_inverted_index.were | 109, 129 |
| abstract_inverted_index.with | 177, 215, 241 |
| abstract_inverted_index.(CHO) | 238 |
| abstract_inverted_index.could | 303 |
| abstract_inverted_index.data. | 125 |
| abstract_inverted_index.form. | 28 |
| abstract_inverted_index.human | 42, 178, 230 |
| abstract_inverted_index.image | 140, 162, 344 |
| abstract_inverted_index.level | 271 |
| abstract_inverted_index.liver | 100, 161 |
| abstract_inverted_index.model | 3, 55 |
| abstract_inverted_index.noise | 302 |
| abstract_inverted_index.often | 9 |
| abstract_inverted_index.study | 335 |
| abstract_inverted_index.task. | 89, 103 |
| abstract_inverted_index.tasks | 48, 72 |
| abstract_inverted_index.these | 33 |
| abstract_inverted_index.three | 185 |
| abstract_inverted_index.trial | 157 |
| abstract_inverted_index.under | 203 |
| abstract_inverted_index.using | 120, 340 |
| abstract_inverted_index.which | 131, 181 |
| abstract_inverted_index.0.812, | 317 |
| abstract_inverted_index.0.987; | 264 |
| abstract_inverted_index.across | 244 |
| abstract_inverted_index.added. | 305 |
| abstract_inverted_index.before | 300 |
| abstract_inverted_index.cases. | 155 |
| abstract_inverted_index.cohort | 152 |
| abstract_inverted_index.exams, | 119 |
| abstract_inverted_index.gauged | 199 |
| abstract_inverted_index.highly | 255 |
| abstract_inverted_index.lesion | 134 |
| abstract_inverted_index.method | 220 |
| abstract_inverted_index.poorer | 294 |
| abstract_inverted_index.random | 20 |
| abstract_inverted_index.sought | 78 |
| abstract_inverted_index.study. | 191, 232 |
| abstract_inverted_index.tasks. | 351 |
| abstract_inverted_index.trials | 147, 226 |
| abstract_inverted_index.types. | 142 |
| abstract_inverted_index.weaker | 313 |
| abstract_inverted_index.DL‐MO | 82, 96, 167, 195, 212, 243, 253, 273, 342 |
| abstract_inverted_index.Methods | 90 |
| abstract_inverted_index.Purpose | 1 |
| abstract_inverted_index.Results | 249 |
| abstract_inverted_index.[0.378, | 321 |
| abstract_inverted_index.[0.942, | 267 |
| abstract_inverted_index.adapted | 92 |
| abstract_inverted_index.between | 308 |
| abstract_inverted_index.curves. | 206 |
| abstract_inverted_index.grouped | 280, 297 |
| abstract_inverted_index.hepatic | 86 |
| abstract_inverted_index.involve | 50 |
| abstract_inverted_index.levels, | 138 |
| abstract_inverted_index.limited | 10 |
| abstract_inverted_index.modeled | 24 |
| abstract_inverted_index.patches | 163 |
| abstract_inverted_index.patient | 123, 151, 348 |
| abstract_inverted_index.predict | 41 |
| abstract_inverted_index.quality | 345 |
| abstract_inverted_index.readily | 38 |
| abstract_inverted_index.twofold | 218 |
| abstract_inverted_index.unclear | 31 |
| abstract_inverted_index.uniform | 13 |
| abstract_inverted_index.varying | 16 |
| abstract_inverted_index.0.955]), | 322 |
| abstract_inverted_index.0.997]). | 268 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Hoteling | 236 |
| abstract_inverted_index.archived | 122 |
| abstract_inverted_index.clinical | 46 |
| abstract_inverted_index.compared | 176, 240 |
| abstract_inverted_index.contrast | 70 |
| abstract_inverted_index.included | 145 |
| abstract_inverted_index.internal | 301 |
| abstract_inverted_index.involved | 132 |
| abstract_inverted_index.observer | 43, 179, 190, 231 |
| abstract_inverted_index.obtained | 183 |
| abstract_inverted_index.positive | 114 |
| abstract_inverted_index.recently | 59, 94 |
| abstract_inverted_index.repeated | 217 |
| abstract_inverted_index.validate | 80 |
| abstract_inverted_index.voxels). | 165 |
| abstract_inverted_index.‐3.3%. | 289 |
| abstract_inverted_index.(DL‐MO) | 57 |
| abstract_inverted_index.Observers | 237 |
| abstract_inverted_index.abdominal | 74, 117, 349 |
| abstract_inverted_index.condition | 144, 173 |
| abstract_inverted_index.developed | 95 |
| abstract_inverted_index.different | 133 |
| abstract_inverted_index.estimated | 214 |
| abstract_inverted_index.framework | 97 |
| abstract_inverted_index.generated | 148 |
| abstract_inverted_index.observers | 4, 56 |
| abstract_inverted_index.potential | 338 |
| abstract_inverted_index.presented | 159, 334 |
| abstract_inverted_index.radiation | 136 |
| abstract_inverted_index.realistic | 51, 113 |
| abstract_inverted_index.validated | 66 |
| abstract_inverted_index.(Pearson's | 261, 314 |
| abstract_inverted_index.(‐29.5%) | 328 |
| abstract_inverted_index.Conclusion | 332 |
| abstract_inverted_index.analytical | 27 |
| abstract_inverted_index.anatomical | 52 |
| abstract_inverted_index.assessment | 346 |
| abstract_inverted_index.background | 14, 17 |
| abstract_inverted_index.calculated | 170 |
| abstract_inverted_index.comparable | 275 |
| abstract_inverted_index.conditions | 128 |
| abstract_inverted_index.correlated | 256 |
| abstract_inverted_index.developed, | 61 |
| abstract_inverted_index.diagnostic | 71 |
| abstract_inverted_index.difference | 287 |
| abstract_inverted_index.generated, | 130 |
| abstract_inverted_index.low‐dose | 116 |
| abstract_inverted_index.metastases | 87, 101 |
| abstract_inverted_index.projection | 124 |
| abstract_inverted_index.synthesize | 112 |
| abstract_inverted_index.techniques | 108 |
| abstract_inverted_index.Channelized | 235 |
| abstract_inverted_index.background. | 53 |
| abstract_inverted_index.challenging | 68 |
| abstract_inverted_index.conditions. | 248 |
| abstract_inverted_index.correlation | 262, 307, 315 |
| abstract_inverted_index.generalized | 39 |
| abstract_inverted_index.performance | 44, 168, 193, 209, 251, 270, 286, 292, 326 |
| abstract_inverted_index.radiologist | 298 |
| abstract_inverted_index.(160×160×5 | 164 |
| abstract_inverted_index.Conventional | 2 |
| abstract_inverted_index.coefficient: | 263, 316 |
| abstract_inverted_index.consequently | 77 |
| abstract_inverted_index.conventional | 34 |
| abstract_inverted_index.demonstrated | 336 |
| abstract_inverted_index.experimental | 127, 247 |
| abstract_inverted_index.localization | 88, 102, 204 |
| abstract_inverted_index.performance, | 180, 299 |
| abstract_inverted_index.radiologists | 187, 197, 260, 311 |
| abstract_inverted_index.significant. | 331 |
| abstract_inverted_index.corresponding | 325 |
| abstract_inverted_index.multi‐slice | 234 |
| abstract_inverted_index.radiologists, | 281 |
| abstract_inverted_index.statistically | 330 |
| abstract_inverted_index.generalization | 208 |
| abstract_inverted_index.low‐contrast | 85 |
| abstract_inverted_index.reconstruction | 141 |
| abstract_inverted_index.sizes/contrasts, | 135 |
| abstract_inverted_index.sub‐specialized | 186 |
| abstract_inverted_index.cross‐validation | 219 |
| abstract_inverted_index.projection‐domain | 106 |
| abstract_inverted_index.previously‐validated | 105 |
| abstract_inverted_index.Deep‐learning‐based | 54 |
| abstract_inverted_index.lesion‐/noise‐insertion | 107 |
| abstract_inverted_index.receiver‐operating‐characteristic | 205 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5047914292 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I1330342723 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.8452896 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |