Detecting Cognitive Fatigue in Subjects with Traumatic Brain Injury from FMRI Scans Using Self-Supervised Learning Article Swipe
Ashish Jaiswal
,
Ashwin Ramesh Babu
,
Mohammad Zaki Zadeh
,
Glenn R. Wylie
,
Fillia Makedon
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3594806.3594868
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3594806.3594868
Understanding cognitive states from fMRI data have yet to be investigated to its full extent due to its complex nature. In this work, the problem of understanding cognitive fatigue among TBI patients has been formulated as a multi-class classification problem. We built a Spatio-temporal encoder model using convolutions and LSTMs as the building blocks to extract spatial features and to model the 4D nature of fMRI scans. To learn a better representation of the data and the condition, we used a self-supervised learning technique called "Contrastive Learning" to pretrain our encoder with a public dataset BOLD5000 and further fine-tuned our labeled dataset to predict cognitive fatigue. Furthermore, we present an fMRI dataset that contains scans from a mix of Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of standardized N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem with different modalities. Besides, the ability of our models to take in raw fMRI scans (noisy images with artifacts output directly from the scanner) eliminates the need to implement a manual signal processing pipeline that varies based on the scanner used. Finally, we study the impact of different brain regions contributing to CF. The proposed technique outperforms the state-of-the-art method by over 13 percent on this dataset.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3594806.3594868
- https://dl.acm.org/doi/pdf/10.1145/3594806.3594868
- OA Status
- gold
- Cited By
- 5
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385732614
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385732614Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3594806.3594868Digital Object Identifier
- Title
-
Detecting Cognitive Fatigue in Subjects with Traumatic Brain Injury from FMRI Scans Using Self-Supervised LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-05Full publication date if available
- Authors
-
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Glenn R. Wylie, Fillia MakedonList of authors in order
- Landing page
-
https://doi.org/10.1145/3594806.3594868Publisher landing page
- PDF URL
-
https://dl.acm.org/doi/pdf/10.1145/3594806.3594868Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://dl.acm.org/doi/pdf/10.1145/3594806.3594868Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Cognition, Pattern recognition (psychology), Neuroimaging, Machine learning, Psychology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4385732614 |
|---|---|
| doi | https://doi.org/10.1145/3594806.3594868 |
| ids.doi | https://doi.org/10.1145/3594806.3594868 |
| ids.openalex | https://openalex.org/W4385732614 |
| fwci | 2.70858544 |
| type | article |
| title | Detecting Cognitive Fatigue in Subjects with Traumatic Brain Injury from FMRI Scans Using Self-Supervised Learning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 90 |
| biblio.first_page | 83 |
| topics[0].id | https://openalex.org/T10645 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9973999857902527 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2711 |
| topics[0].subfield.display_name | Emergency Medicine |
| topics[0].display_name | Cardiac Arrest and Resuscitation |
| topics[1].id | https://openalex.org/T10416 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9969000220298767 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2713 |
| topics[1].subfield.display_name | Epidemiology |
| topics[1].display_name | Traumatic Brain Injury Research |
| topics[2].id | https://openalex.org/T11184 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.992900013923645 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2735 |
| topics[2].subfield.display_name | Pediatrics, Perinatology and Child Health |
| topics[2].display_name | Neonatal and fetal brain pathology |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7195054888725281 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6050882339477539 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C169900460 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6041783690452576 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2200417 |
| concepts[2].display_name | Cognition |
| concepts[3].id | https://openalex.org/C153180895 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4713532328605652 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[3].display_name | Pattern recognition (psychology) |
| concepts[4].id | https://openalex.org/C58693492 |
| concepts[4].level | 2 |
| concepts[4].score | 0.44146138429641724 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q551875 |
| concepts[4].display_name | Neuroimaging |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.36157387495040894 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C15744967 |
| concepts[6].level | 0 |
| concepts[6].score | 0.15187418460845947 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[6].display_name | Psychology |
| concepts[7].id | https://openalex.org/C169760540 |
| concepts[7].level | 1 |
| concepts[7].score | 0.10289135575294495 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[7].display_name | Neuroscience |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7195054888725281 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.6050882339477539 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/cognition |
| keywords[2].score | 0.6041783690452576 |
| keywords[2].display_name | Cognition |
| keywords[3].id | https://openalex.org/keywords/pattern-recognition |
| keywords[3].score | 0.4713532328605652 |
| keywords[3].display_name | Pattern recognition (psychology) |
| keywords[4].id | https://openalex.org/keywords/neuroimaging |
| keywords[4].score | 0.44146138429641724 |
| keywords[4].display_name | Neuroimaging |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.36157387495040894 |
| keywords[5].display_name | Machine learning |
| keywords[6].id | https://openalex.org/keywords/psychology |
| keywords[6].score | 0.15187418460845947 |
| keywords[6].display_name | Psychology |
| keywords[7].id | https://openalex.org/keywords/neuroscience |
| keywords[7].score | 0.10289135575294495 |
| keywords[7].display_name | Neuroscience |
| language | en |
| locations[0].id | doi:10.1145/3594806.3594868 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://dl.acm.org/doi/pdf/10.1145/3594806.3594868 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-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 | Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments |
| locations[0].landing_page_url | https://doi.org/10.1145/3594806.3594868 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5047380782 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6613-4955 |
| authorships[0].author.display_name | Ashish Jaiswal |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I189196454 |
| authorships[0].affiliations[0].raw_affiliation_string | The University of Texas at Arlington, United States |
| authorships[0].institutions[0].id | https://openalex.org/I189196454 |
| authorships[0].institutions[0].ror | https://ror.org/019kgqr73 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I189196454 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | The University of Texas at Arlington |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ashish Jaiswal |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | The University of Texas at Arlington, United States |
| authorships[1].author.id | https://openalex.org/A5023297665 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4131-7659 |
| authorships[1].author.display_name | Ashwin Ramesh Babu |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I189196454 |
| authorships[1].affiliations[0].raw_affiliation_string | The University of Texas at Arlington, United States |
| authorships[1].institutions[0].id | https://openalex.org/I189196454 |
| authorships[1].institutions[0].ror | https://ror.org/019kgqr73 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I189196454 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | The University of Texas at Arlington |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ashwin Ramesh Babu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | The University of Texas at Arlington, United States |
| authorships[2].author.id | https://openalex.org/A5072555824 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1504-7536 |
| authorships[2].author.display_name | Mohammad Zaki Zadeh |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I189196454 |
| authorships[2].affiliations[0].raw_affiliation_string | The University of Texas at Arlington, United States |
| authorships[2].institutions[0].id | https://openalex.org/I189196454 |
| authorships[2].institutions[0].ror | https://ror.org/019kgqr73 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I189196454 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | The University of Texas at Arlington |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mohammad Zaki Zadeh |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | The University of Texas at Arlington, United States |
| authorships[3].author.id | https://openalex.org/A5008671331 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-7061-6658 |
| authorships[3].author.display_name | Glenn R. Wylie |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I250153783 |
| authorships[3].affiliations[0].raw_affiliation_string | Kessler Foundation, USA |
| authorships[3].institutions[0].id | https://openalex.org/I250153783 |
| authorships[3].institutions[0].ror | https://ror.org/05hacyq28 |
| authorships[3].institutions[0].type | nonprofit |
| authorships[3].institutions[0].lineage | https://openalex.org/I250153783 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Kessler Foundation |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Glenn Wylie |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Kessler Foundation, USA |
| authorships[4].author.id | https://openalex.org/A5044363105 |
| authorships[4].author.orcid | https://orcid.org/0009-0001-8831-6872 |
| authorships[4].author.display_name | Fillia Makedon |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I189196454 |
| authorships[4].affiliations[0].raw_affiliation_string | The University of Texas at Arlington, USA |
| authorships[4].institutions[0].id | https://openalex.org/I189196454 |
| authorships[4].institutions[0].ror | https://ror.org/019kgqr73 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I189196454 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | The University of Texas at Arlington |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Fillia Makedon |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | The University of Texas at Arlington, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://dl.acm.org/doi/pdf/10.1145/3594806.3594868 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Detecting Cognitive Fatigue in Subjects with Traumatic Brain Injury from FMRI Scans Using Self-Supervised Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10645 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9973999857902527 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2711 |
| primary_topic.subfield.display_name | Emergency Medicine |
| primary_topic.display_name | Cardiac Arrest and Resuscitation |
| related_works | https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W3046775127, https://openalex.org/W3107602296, https://openalex.org/W4394896187, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W4386286677, https://openalex.org/W4364306694, https://openalex.org/W4312192474 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1145/3594806.3594868 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://dl.acm.org/doi/pdf/10.1145/3594806.3594868 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-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 | Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments |
| best_oa_location.landing_page_url | https://doi.org/10.1145/3594806.3594868 |
| primary_location.id | doi:10.1145/3594806.3594868 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://dl.acm.org/doi/pdf/10.1145/3594806.3594868 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-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 | Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments |
| primary_location.landing_page_url | https://doi.org/10.1145/3594806.3594868 |
| publication_date | 2023-07-05 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2056811629, https://openalex.org/W2097656614, https://openalex.org/W2128577709, https://openalex.org/W1952680005, https://openalex.org/W1978689156, https://openalex.org/W1973717649, https://openalex.org/W2674888069, https://openalex.org/W1976122501, https://openalex.org/W3039748807, https://openalex.org/W3038656869, https://openalex.org/W2963447570, https://openalex.org/W2063665995, https://openalex.org/W2144499799, https://openalex.org/W3035524453, https://openalex.org/W2937940522, https://openalex.org/W3114632476, https://openalex.org/W4285007592, https://openalex.org/W1538131130, https://openalex.org/W2043806828, https://openalex.org/W2946763314, https://openalex.org/W2023057624, https://openalex.org/W3004371658, https://openalex.org/W4285004879, https://openalex.org/W2158485497, https://openalex.org/W3094364584, https://openalex.org/W2509095792, https://openalex.org/W1642699591, https://openalex.org/W2086699475, https://openalex.org/W3213351100, https://openalex.org/W2782522902, https://openalex.org/W3039491339, https://openalex.org/W3090499854 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 36, 42, 69, 80, 92, 116, 130, 140, 187 |
| abstract_inverted_index.13 | 220 |
| abstract_inverted_index.4D | 62 |
| abstract_inverted_index.In | 20 |
| abstract_inverted_index.To | 67 |
| abstract_inverted_index.We | 40 |
| abstract_inverted_index.an | 109 |
| abstract_inverted_index.as | 35, 50 |
| abstract_inverted_index.be | 9 |
| abstract_inverted_index.by | 218 |
| abstract_inverted_index.in | 169 |
| abstract_inverted_index.of | 25, 64, 72, 118, 132, 164, 204 |
| abstract_inverted_index.on | 195, 222 |
| abstract_inverted_index.to | 8, 11, 16, 54, 59, 87, 102, 143, 154, 167, 185, 209 |
| abstract_inverted_index.we | 78, 107, 200 |
| abstract_inverted_index.CF. | 210 |
| abstract_inverted_index.TBI | 30 |
| abstract_inverted_index.The | 211 |
| abstract_inverted_index.and | 48, 58, 75, 96, 124, 150 |
| abstract_inverted_index.due | 15 |
| abstract_inverted_index.has | 32 |
| abstract_inverted_index.its | 12, 17 |
| abstract_inverted_index.mix | 117 |
| abstract_inverted_index.our | 89, 99, 165 |
| abstract_inverted_index.raw | 170 |
| abstract_inverted_index.the | 23, 51, 61, 73, 76, 162, 180, 183, 196, 202, 215 |
| abstract_inverted_index.yet | 7 |
| abstract_inverted_index.This | 137 |
| abstract_inverted_index.been | 33 |
| abstract_inverted_index.data | 5, 74, 149 |
| abstract_inverted_index.fMRI | 4, 65, 110, 148, 171 |
| abstract_inverted_index.from | 3, 115, 147, 179 |
| abstract_inverted_index.full | 13 |
| abstract_inverted_index.have | 6 |
| abstract_inverted_index.need | 184 |
| abstract_inverted_index.over | 219 |
| abstract_inverted_index.take | 168 |
| abstract_inverted_index.that | 112, 192 |
| abstract_inverted_index.this | 21, 156, 223 |
| abstract_inverted_index.used | 79 |
| abstract_inverted_index.with | 91, 158, 175 |
| abstract_inverted_index.(HCs) | 127 |
| abstract_inverted_index.(TBI) | 122 |
| abstract_inverted_index.Brain | 120 |
| abstract_inverted_index.LSTMs | 49 |
| abstract_inverted_index.among | 29 |
| abstract_inverted_index.based | 194 |
| abstract_inverted_index.beats | 151 |
| abstract_inverted_index.brain | 206 |
| abstract_inverted_index.built | 41 |
| abstract_inverted_index.learn | 68 |
| abstract_inverted_index.model | 45, 60 |
| abstract_inverted_index.scans | 114, 172 |
| abstract_inverted_index.solve | 155 |
| abstract_inverted_index.study | 201 |
| abstract_inverted_index.used. | 198 |
| abstract_inverted_index.using | 46 |
| abstract_inverted_index.while | 128 |
| abstract_inverted_index.work, | 22 |
| abstract_inverted_index.(noisy | 173 |
| abstract_inverted_index.Injury | 121 |
| abstract_inverted_index.N-back | 134 |
| abstract_inverted_index.better | 70 |
| abstract_inverted_index.blocks | 53 |
| abstract_inverted_index.called | 84 |
| abstract_inverted_index.extent | 14 |
| abstract_inverted_index.images | 174 |
| abstract_inverted_index.impact | 203 |
| abstract_inverted_index.manual | 188 |
| abstract_inverted_index.method | 138, 217 |
| abstract_inverted_index.models | 166 |
| abstract_inverted_index.nature | 63 |
| abstract_inverted_index.output | 177 |
| abstract_inverted_index.public | 93 |
| abstract_inverted_index.scans. | 66 |
| abstract_inverted_index.series | 131 |
| abstract_inverted_index.signal | 189 |
| abstract_inverted_index.states | 2 |
| abstract_inverted_index.tasks. | 136 |
| abstract_inverted_index.varies | 193 |
| abstract_inverted_index.ability | 163 |
| abstract_inverted_index.analyze | 144 |
| abstract_inverted_index.complex | 18 |
| abstract_inverted_index.dataset | 94, 101, 111 |
| abstract_inverted_index.encoder | 44, 90 |
| abstract_inverted_index.extract | 55 |
| abstract_inverted_index.fatigue | 28, 146 |
| abstract_inverted_index.further | 97 |
| abstract_inverted_index.healthy | 125 |
| abstract_inverted_index.labeled | 100 |
| abstract_inverted_index.nature. | 19 |
| abstract_inverted_index.percent | 221 |
| abstract_inverted_index.predict | 103 |
| abstract_inverted_index.present | 108 |
| abstract_inverted_index.problem | 24, 157 |
| abstract_inverted_index.regions | 207 |
| abstract_inverted_index.scanner | 197 |
| abstract_inverted_index.spatial | 56 |
| abstract_inverted_index.BOLD5000 | 95 |
| abstract_inverted_index.Besides, | 161 |
| abstract_inverted_index.Finally, | 199 |
| abstract_inverted_index.building | 52 |
| abstract_inverted_index.contains | 113 |
| abstract_inverted_index.controls | 126 |
| abstract_inverted_index.dataset. | 224 |
| abstract_inverted_index.directly | 178 |
| abstract_inverted_index.fatigue. | 105 |
| abstract_inverted_index.features | 57 |
| abstract_inverted_index.learning | 82 |
| abstract_inverted_index.patients | 31, 123 |
| abstract_inverted_index.pipeline | 191 |
| abstract_inverted_index.pretrain | 88 |
| abstract_inverted_index.previous | 152 |
| abstract_inverted_index.problem. | 39 |
| abstract_inverted_index.proposed | 212 |
| abstract_inverted_index.scanner) | 181 |
| abstract_inverted_index.Learning" | 86 |
| abstract_inverted_index.Traumatic | 119 |
| abstract_inverted_index.artifacts | 176 |
| abstract_inverted_index.cognitive | 1, 27, 104, 135, 145 |
| abstract_inverted_index.different | 159, 205 |
| abstract_inverted_index.implement | 186 |
| abstract_inverted_index.technique | 83, 142, 213 |
| abstract_inverted_index.approaches | 153 |
| abstract_inverted_index.condition, | 77 |
| abstract_inverted_index.eliminates | 182 |
| abstract_inverted_index.fine-tuned | 98 |
| abstract_inverted_index.formulated | 34 |
| abstract_inverted_index.performing | 129 |
| abstract_inverted_index.processing | 190 |
| abstract_inverted_index.establishes | 139 |
| abstract_inverted_index.modalities. | 160 |
| abstract_inverted_index.multi-class | 37 |
| abstract_inverted_index.outperforms | 214 |
| abstract_inverted_index."Contrastive | 85 |
| abstract_inverted_index.Furthermore, | 106 |
| abstract_inverted_index.contributing | 208 |
| abstract_inverted_index.convolutions | 47 |
| abstract_inverted_index.investigated | 10 |
| abstract_inverted_index.standardized | 133 |
| abstract_inverted_index.Understanding | 0 |
| abstract_inverted_index.understanding | 26 |
| abstract_inverted_index.classification | 38 |
| abstract_inverted_index.representation | 71 |
| abstract_inverted_index.Spatio-temporal | 43 |
| abstract_inverted_index.self-supervised | 81 |
| abstract_inverted_index.state-of-the-art | 141, 216 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.8598097 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |