Physics-informed Machine Learning for Medical Image Analysis Article Swipe
Chayan Banerjee
,
Kien Nguyen
,
Olivier Salvado
,
Truyen Tran
,
Clinton Fookes
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3768158
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3768158
The incorporation of physical information in machine learning frameworks is transforming medical image analysis (MIA). Integrating fundamental knowledge and governing physical laws not only improves analysis performance but also enhances the model’s robustness and interpretability. This work presents a systematic review of over 100 papers on the utility of PINNs dedicated to MIA (PIMIA) tasks. We propose a unified taxonomy to investigate what physics knowledge and processes are modeled, how they are represented, and the strategies to incorporate them into MIA models. We delve deep into a wide range of image analysis tasks, from imaging, generation, prediction, inverse imaging (super-resolution and reconstruction), registration, and image analysis (segmentation and classification). For each task, we thoroughly examine and present the central physics-guided operation, the region of interest (with respect to human anatomy), the corresponding imaging modality, the datasets used for model training, the deep network architectures employed, and the primary physical processes, equations, or principles utilized. Additionally, we also introduce a novel metric to compare the performance of PIMIA methods across different tasks and datasets. Based on this review, we summarize and distill our perspectives on the challenges, and highlight open research questions and directions for future research.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3768158
- OA Status
- hybrid
- References
- 84
- OpenAlex ID
- https://openalex.org/W4414179233
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4414179233Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3768158Digital Object Identifier
- Title
-
Physics-informed Machine Learning for Medical Image AnalysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-15Full publication date if available
- Authors
-
Chayan Banerjee, Kien Nguyen, Olivier Salvado, Truyen Tran, Clinton FookesList of authors in order
- Landing page
-
https://doi.org/10.1145/3768158Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1145/3768158Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
84Number of works referenced by this work
Full payload
| id | https://openalex.org/W4414179233 |
|---|---|
| doi | https://doi.org/10.1145/3768158 |
| ids.doi | https://doi.org/10.1145/3768158 |
| ids.openalex | https://openalex.org/W4414179233 |
| fwci | 0.0 |
| type | article |
| title | Physics-informed Machine Learning for Medical Image Analysis |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11206 |
| topics[0].field.id | https://openalex.org/fields/31 |
| topics[0].field.display_name | Physics and Astronomy |
| topics[0].score | 0.9944000244140625 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3109 |
| topics[0].subfield.display_name | Statistical and Nonlinear Physics |
| topics[0].display_name | Model Reduction and Neural Networks |
| topics[1].id | https://openalex.org/T10271 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.982200026512146 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1908 |
| topics[1].subfield.display_name | Geophysics |
| topics[1].display_name | Seismic Imaging and Inversion Techniques |
| topics[2].id | https://openalex.org/T13650 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9772999882698059 |
| 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 | Computational Physics and Python Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| language | en |
| locations[0].id | doi:10.1145/3768158 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S157921468 |
| locations[0].source.issn | 0360-0300, 1557-7341 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0360-0300 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | ACM Computing Surveys |
| locations[0].source.host_organization | https://openalex.org/P4310319798 |
| locations[0].source.host_organization_name | Association for Computing Machinery |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319798 |
| locations[0].source.host_organization_lineage_names | Association for Computing Machinery |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | ACM Computing Surveys |
| locations[0].landing_page_url | https://doi.org/10.1145/3768158 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5043976016 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1039-3744 |
| authorships[0].author.display_name | Chayan Banerjee |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Electrical Engneering and Robotics, Queensland University of Technology |
| authorships[0].institutions[0].id | https://openalex.org/I160993911 |
| authorships[0].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | Queensland University of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chayan Banerjee |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Electrical Engneering and Robotics, Queensland University of Technology |
| authorships[1].author.id | https://openalex.org/A5038839161 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3466-9218 |
| authorships[1].author.display_name | Kien Nguyen |
| authorships[1].countries | AU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Electrical Engneering and Robotics, Queensland University of Technology |
| authorships[1].institutions[0].id | https://openalex.org/I160993911 |
| authorships[1].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[1].institutions[0].country_code | AU |
| authorships[1].institutions[0].display_name | Queensland University of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Kien Nguyen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Electrical Engneering and Robotics, Queensland University of Technology |
| authorships[2].author.id | https://openalex.org/A5025220020 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2720-8739 |
| authorships[2].author.display_name | Olivier Salvado |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Electrical Engineering and Robotics, Queensland University of Technology |
| authorships[2].institutions[0].id | https://openalex.org/I160993911 |
| authorships[2].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[2].institutions[0].country_code | AU |
| authorships[2].institutions[0].display_name | Queensland University of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Olivier Salvado |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Electrical Engineering and Robotics, Queensland University of Technology |
| authorships[3].author.id | https://openalex.org/A5085471517 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6531-8907 |
| authorships[3].author.display_name | Truyen Tran |
| authorships[3].countries | AU |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I149704539 |
| authorships[3].affiliations[0].raw_affiliation_string | Deakin University |
| authorships[3].institutions[0].id | https://openalex.org/I149704539 |
| authorships[3].institutions[0].ror | https://ror.org/02czsnj07 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I149704539 |
| authorships[3].institutions[0].country_code | AU |
| authorships[3].institutions[0].display_name | Deakin University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Truyen Tran |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Deakin University |
| authorships[4].author.id | https://openalex.org/A5034095159 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-8515-6324 |
| authorships[4].author.display_name | Clinton Fookes |
| authorships[4].countries | AU |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I160993911 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Electrical Engneering and Robotics, Queensland University of Technology |
| authorships[4].institutions[0].id | https://openalex.org/I160993911 |
| authorships[4].institutions[0].ror | https://ror.org/03pnv4752 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I160993911 |
| authorships[4].institutions[0].country_code | AU |
| authorships[4].institutions[0].display_name | Queensland University of Technology |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Clinton Fookes |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | School of Electrical Engneering and Robotics, Queensland University of Technology |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1145/3768158 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Physics-informed Machine Learning for Medical Image Analysis |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11206 |
| primary_topic.field.id | https://openalex.org/fields/31 |
| primary_topic.field.display_name | Physics and Astronomy |
| primary_topic.score | 0.9944000244140625 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3109 |
| primary_topic.subfield.display_name | Statistical and Nonlinear Physics |
| primary_topic.display_name | Model Reduction and Neural Networks |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1145/3768158 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S157921468 |
| best_oa_location.source.issn | 0360-0300, 1557-7341 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0360-0300 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | ACM Computing Surveys |
| best_oa_location.source.host_organization | https://openalex.org/P4310319798 |
| best_oa_location.source.host_organization_name | Association for Computing Machinery |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319798 |
| best_oa_location.source.host_organization_lineage_names | Association for Computing Machinery |
| best_oa_location.license | cc-by |
| 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 |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | ACM Computing Surveys |
| best_oa_location.landing_page_url | https://doi.org/10.1145/3768158 |
| primary_location.id | doi:10.1145/3768158 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S157921468 |
| primary_location.source.issn | 0360-0300, 1557-7341 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0360-0300 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | ACM Computing Surveys |
| primary_location.source.host_organization | https://openalex.org/P4310319798 |
| primary_location.source.host_organization_name | Association for Computing Machinery |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319798 |
| primary_location.source.host_organization_lineage_names | Association for Computing Machinery |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | ACM Computing Surveys |
| primary_location.landing_page_url | https://doi.org/10.1145/3768158 |
| publication_date | 2025-09-15 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4300942166, https://openalex.org/W4400089912, https://openalex.org/W4387447711, https://openalex.org/W3213990473, https://openalex.org/W2979878619, https://openalex.org/W3155397377, https://openalex.org/W4319315319, https://openalex.org/W3096165964, https://openalex.org/W4386046221, https://openalex.org/W4385798723, https://openalex.org/W4288039037, https://openalex.org/W4376057663, https://openalex.org/W4403069831, https://openalex.org/W3086033911, https://openalex.org/W3005933814, https://openalex.org/W2794022343, https://openalex.org/W4402726968, https://openalex.org/W4380084939, https://openalex.org/W4398782184, https://openalex.org/W4319300096, https://openalex.org/W4399903427, https://openalex.org/W4210349282, https://openalex.org/W3167534721, https://openalex.org/W4396690897, https://openalex.org/W4307309751, https://openalex.org/W4410487818, https://openalex.org/W3199091656, https://openalex.org/W4309436702, https://openalex.org/W4386857800, https://openalex.org/W3163993681, https://openalex.org/W3093190107, https://openalex.org/W4221114735, https://openalex.org/W4318479582, https://openalex.org/W2973886134, https://openalex.org/W3006982737, https://openalex.org/W2534240011, https://openalex.org/W4384830375, https://openalex.org/W2592929672, https://openalex.org/W2995286437, https://openalex.org/W3158629041, https://openalex.org/W4388839581, https://openalex.org/W4385703550, https://openalex.org/W2979786244, https://openalex.org/W4402755854, https://openalex.org/W3167256391, https://openalex.org/W4379660284, https://openalex.org/W4362594575, https://openalex.org/W4384206867, https://openalex.org/W4313050591, https://openalex.org/W3017305623, https://openalex.org/W4286493946, https://openalex.org/W4309717547, https://openalex.org/W4390048458, https://openalex.org/W4220937057, https://openalex.org/W4393194546, https://openalex.org/W3164830908, https://openalex.org/W3039471517, https://openalex.org/W4392740133, https://openalex.org/W4379660261, https://openalex.org/W3203200434, https://openalex.org/W3183198896, https://openalex.org/W3108748322, https://openalex.org/W4210729890, https://openalex.org/W2946794331, https://openalex.org/W4391775435, https://openalex.org/W2980868302, https://openalex.org/W2963716063, https://openalex.org/W4295782861, https://openalex.org/W4400680269, https://openalex.org/W4403090479, https://openalex.org/W4313018984, https://openalex.org/W4385383119, https://openalex.org/W2995697467, https://openalex.org/W3074741277, https://openalex.org/W4321437922, https://openalex.org/W4381571755, https://openalex.org/W3100075319, https://openalex.org/W3133294546, https://openalex.org/W3134475970, https://openalex.org/W3129530645, https://openalex.org/W4313524971, https://openalex.org/W4225716327, https://openalex.org/W3103922709, https://openalex.org/W3015168408 |
| referenced_works_count | 84 |
| abstract_inverted_index.a | 38, 57, 86, 158 |
| abstract_inverted_index.We | 55, 82 |
| abstract_inverted_index.in | 5 |
| abstract_inverted_index.is | 9 |
| abstract_inverted_index.of | 2, 41, 48, 89, 123, 165 |
| abstract_inverted_index.on | 45, 174, 183 |
| abstract_inverted_index.or | 151 |
| abstract_inverted_index.to | 51, 60, 76, 127, 161 |
| abstract_inverted_index.we | 112, 155, 177 |
| abstract_inverted_index.100 | 43 |
| abstract_inverted_index.For | 109 |
| abstract_inverted_index.MIA | 52, 80 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 18, 33, 65, 73, 100, 103, 107, 115, 145, 171, 179, 186, 191 |
| abstract_inverted_index.are | 67, 71 |
| abstract_inverted_index.but | 27 |
| abstract_inverted_index.for | 137, 193 |
| abstract_inverted_index.how | 69 |
| abstract_inverted_index.not | 22 |
| abstract_inverted_index.our | 181 |
| abstract_inverted_index.the | 30, 46, 74, 117, 121, 130, 134, 140, 146, 163, 184 |
| abstract_inverted_index.This | 35 |
| abstract_inverted_index.also | 28, 156 |
| abstract_inverted_index.deep | 84, 141 |
| abstract_inverted_index.each | 110 |
| abstract_inverted_index.from | 93 |
| abstract_inverted_index.into | 79, 85 |
| abstract_inverted_index.laws | 21 |
| abstract_inverted_index.only | 23 |
| abstract_inverted_index.open | 188 |
| abstract_inverted_index.over | 42 |
| abstract_inverted_index.them | 78 |
| abstract_inverted_index.they | 70 |
| abstract_inverted_index.this | 175 |
| abstract_inverted_index.used | 136 |
| abstract_inverted_index.what | 62 |
| abstract_inverted_index.wide | 87 |
| abstract_inverted_index.work | 36 |
| abstract_inverted_index.(with | 125 |
| abstract_inverted_index.Based | 173 |
| abstract_inverted_index.PIMIA | 166 |
| abstract_inverted_index.PINNs | 49 |
| abstract_inverted_index.delve | 83 |
| abstract_inverted_index.human | 128 |
| abstract_inverted_index.image | 12, 90, 104 |
| abstract_inverted_index.model | 138 |
| abstract_inverted_index.novel | 159 |
| abstract_inverted_index.range | 88 |
| abstract_inverted_index.task, | 111 |
| abstract_inverted_index.tasks | 170 |
| abstract_inverted_index.(MIA). | 14 |
| abstract_inverted_index.across | 168 |
| abstract_inverted_index.future | 194 |
| abstract_inverted_index.metric | 160 |
| abstract_inverted_index.papers | 44 |
| abstract_inverted_index.region | 122 |
| abstract_inverted_index.review | 40 |
| abstract_inverted_index.tasks, | 92 |
| abstract_inverted_index.tasks. | 54 |
| abstract_inverted_index.(PIMIA) | 53 |
| abstract_inverted_index.central | 118 |
| abstract_inverted_index.compare | 162 |
| abstract_inverted_index.distill | 180 |
| abstract_inverted_index.examine | 114 |
| abstract_inverted_index.imaging | 98, 132 |
| abstract_inverted_index.inverse | 97 |
| abstract_inverted_index.machine | 6 |
| abstract_inverted_index.medical | 11 |
| abstract_inverted_index.methods | 167 |
| abstract_inverted_index.models. | 81 |
| abstract_inverted_index.network | 142 |
| abstract_inverted_index.physics | 63 |
| abstract_inverted_index.present | 116 |
| abstract_inverted_index.primary | 147 |
| abstract_inverted_index.propose | 56 |
| abstract_inverted_index.respect | 126 |
| abstract_inverted_index.review, | 176 |
| abstract_inverted_index.unified | 58 |
| abstract_inverted_index.utility | 47 |
| abstract_inverted_index.analysis | 13, 25, 91, 105 |
| abstract_inverted_index.datasets | 135 |
| abstract_inverted_index.enhances | 29 |
| abstract_inverted_index.imaging, | 94 |
| abstract_inverted_index.improves | 24 |
| abstract_inverted_index.interest | 124 |
| abstract_inverted_index.learning | 7 |
| abstract_inverted_index.modeled, | 68 |
| abstract_inverted_index.physical | 3, 20, 148 |
| abstract_inverted_index.presents | 37 |
| abstract_inverted_index.research | 189 |
| abstract_inverted_index.taxonomy | 59 |
| abstract_inverted_index.anatomy), | 129 |
| abstract_inverted_index.datasets. | 172 |
| abstract_inverted_index.dedicated | 50 |
| abstract_inverted_index.different | 169 |
| abstract_inverted_index.employed, | 144 |
| abstract_inverted_index.governing | 19 |
| abstract_inverted_index.highlight | 187 |
| abstract_inverted_index.introduce | 157 |
| abstract_inverted_index.knowledge | 17, 64 |
| abstract_inverted_index.modality, | 133 |
| abstract_inverted_index.model’s | 31 |
| abstract_inverted_index.processes | 66 |
| abstract_inverted_index.questions | 190 |
| abstract_inverted_index.research. | 195 |
| abstract_inverted_index.summarize | 178 |
| abstract_inverted_index.training, | 139 |
| abstract_inverted_index.utilized. | 153 |
| abstract_inverted_index.directions | 192 |
| abstract_inverted_index.equations, | 150 |
| abstract_inverted_index.frameworks | 8 |
| abstract_inverted_index.operation, | 120 |
| abstract_inverted_index.principles | 152 |
| abstract_inverted_index.processes, | 149 |
| abstract_inverted_index.robustness | 32 |
| abstract_inverted_index.strategies | 75 |
| abstract_inverted_index.systematic | 39 |
| abstract_inverted_index.thoroughly | 113 |
| abstract_inverted_index.Integrating | 15 |
| abstract_inverted_index.challenges, | 185 |
| abstract_inverted_index.fundamental | 16 |
| abstract_inverted_index.generation, | 95 |
| abstract_inverted_index.incorporate | 77 |
| abstract_inverted_index.information | 4 |
| abstract_inverted_index.investigate | 61 |
| abstract_inverted_index.performance | 26, 164 |
| abstract_inverted_index.prediction, | 96 |
| abstract_inverted_index.perspectives | 182 |
| abstract_inverted_index.represented, | 72 |
| abstract_inverted_index.transforming | 10 |
| abstract_inverted_index.(segmentation | 106 |
| abstract_inverted_index.Additionally, | 154 |
| abstract_inverted_index.architectures | 143 |
| abstract_inverted_index.corresponding | 131 |
| abstract_inverted_index.incorporation | 1 |
| abstract_inverted_index.registration, | 102 |
| abstract_inverted_index.physics-guided | 119 |
| abstract_inverted_index.classification). | 108 |
| abstract_inverted_index.reconstruction), | 101 |
| abstract_inverted_index.(super-resolution | 99 |
| abstract_inverted_index.interpretability. | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.33986025 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |