Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery Article Swipe
David Black
,
Jaidev Gill
,
Andrew Xie
,
Benoît Liquet
,
Antonio Di Ieva
,
Walter Stummer
,
Eric Suero Molina
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.03761
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.03761
Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified linear methods that are unable to capture the non-linear, wavelength-dependent phenomena that must be modeled for accurate recovery of fluorophore abundances. We therefore propose two deep learning models for correction and unmixing, which can account for the nonlinear effects and produce more accurate estimates of abundances. Both models use an autoencoder-like architecture to process the captured spectra. One is trained with protoporphyrin IX (PpIX) concentration labels. The other undergoes semi-supervised training, first learning hyperspectral unmixing self-supervised and then learning to correct fluorescence emission spectra for heterogeneous optical and geometric properties using a reference white-light reflectance spectrum in a few-shot manner. The models were evaluated against phantom and pig brain data with known PpIX concentration; the supervised model achieved Pearson correlation coefficients (R values) between the known and computed PpIX concentrations of 0.997 and 0.990, respectively, whereas the classical approach achieved only 0.93 and 0.82. The semi-supervised approach's R values were 0.98 and 0.91, respectively. On human data, the semi-supervised model gives qualitatively more realistic results than the classical method, better removing bright spots of specular reflectance and reducing the variance in PpIX abundance over biopsies that should be relatively homogeneous. These results show promise for using deep learning to improve HSI in fluorescence-guided neurosurgery.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.03761
- https://arxiv.org/pdf/2402.03761
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391631760
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4391631760Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2402.03761Digital Object Identifier
- Title
-
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor SurgeryWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-06Full publication date if available
- Authors
-
David Black, Jaidev Gill, Andrew Xie, Benoît Liquet, Antonio Di Ieva, Walter Stummer, Eric Suero MolinaList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.03761Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2402.03761Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2402.03761Direct OA link when available
- Concepts
-
Hyperspectral imaging, Artificial intelligence, Deep learning, Computer science, Neuroimaging, Pattern recognition (psychology), Computer vision, Psychology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4391631760 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2402.03761 |
| ids.doi | https://doi.org/10.48550/arxiv.2402.03761 |
| ids.openalex | https://openalex.org/W4391631760 |
| fwci | |
| type | preprint |
| title | Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12702 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9926000237464905 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2808 |
| topics[0].subfield.display_name | Neurology |
| topics[0].display_name | Brain Tumor Detection and Classification |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C159078339 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8084578514099121 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q959005 |
| concepts[0].display_name | Hyperspectral imaging |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.643357515335083 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C108583219 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5148343443870544 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[2].display_name | Deep learning |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.4519686996936798 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C58693492 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4273563623428345 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q551875 |
| concepts[4].display_name | Neuroimaging |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.3665738105773926 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C31972630 |
| concepts[6].level | 1 |
| concepts[6].score | 0.34540343284606934 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[6].display_name | Computer vision |
| concepts[7].id | https://openalex.org/C15744967 |
| concepts[7].level | 0 |
| concepts[7].score | 0.33914995193481445 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[7].display_name | Psychology |
| concepts[8].id | https://openalex.org/C169760540 |
| concepts[8].level | 1 |
| concepts[8].score | 0.30236291885375977 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[8].display_name | Neuroscience |
| keywords[0].id | https://openalex.org/keywords/hyperspectral-imaging |
| keywords[0].score | 0.8084578514099121 |
| keywords[0].display_name | Hyperspectral imaging |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.643357515335083 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/deep-learning |
| keywords[2].score | 0.5148343443870544 |
| keywords[2].display_name | Deep learning |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.4519686996936798 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/neuroimaging |
| keywords[4].score | 0.4273563623428345 |
| keywords[4].display_name | Neuroimaging |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.3665738105773926 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/computer-vision |
| keywords[6].score | 0.34540343284606934 |
| keywords[6].display_name | Computer vision |
| keywords[7].id | https://openalex.org/keywords/psychology |
| keywords[7].score | 0.33914995193481445 |
| keywords[7].display_name | Psychology |
| keywords[8].id | https://openalex.org/keywords/neuroscience |
| keywords[8].score | 0.30236291885375977 |
| keywords[8].display_name | Neuroscience |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2402.03761 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2402.03761 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2402.03761 |
| locations[1].id | doi:10.48550/arxiv.2402.03761 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2402.03761 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5007609506 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6907-9851 |
| authorships[0].author.display_name | David Black |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Black, David |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5104260050 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Jaidev Gill |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Gill, Jaidev |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5104240861 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Andrew Xie |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Xie, Andrew |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5040962966 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8136-2294 |
| authorships[3].author.display_name | Benoît Liquet |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Liquet, Benoit |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5035155009 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5341-5416 |
| authorships[4].author.display_name | Antonio Di Ieva |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Di leva, Antonio |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5064834985 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-3460-0640 |
| authorships[5].author.display_name | Walter Stummer |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Stummer, Walter |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5016371380 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-5652-2105 |
| authorships[6].author.display_name | Eric Suero Molina |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Molina, Eric Suero |
| authorships[6].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2402.03761 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12702 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9926000237464905 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2808 |
| primary_topic.subfield.display_name | Neurology |
| primary_topic.display_name | Brain Tumor Detection and Classification |
| related_works | https://openalex.org/W2072166414, https://openalex.org/W3209970181, https://openalex.org/W2070598848, https://openalex.org/W3034375524, https://openalex.org/W2060875994, https://openalex.org/W2027399350, https://openalex.org/W2044184146, https://openalex.org/W4313014865, https://openalex.org/W2019190440, https://openalex.org/W2343470940 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2402.03761 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| 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 | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2402.03761 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2402.03761 |
| primary_location.id | pmh:oai:arXiv.org:2402.03761 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2402.03761 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2402.03761 |
| publication_date | 2024-02-06 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.R | 186 |
| abstract_inverted_index.a | 130, 136 |
| abstract_inverted_index.(R | 160 |
| abstract_inverted_index.IX | 101 |
| abstract_inverted_index.On | 193 |
| abstract_inverted_index.We | 60 |
| abstract_inverted_index.an | 88 |
| abstract_inverted_index.be | 52, 226 |
| abstract_inverted_index.in | 35, 135, 219, 240 |
| abstract_inverted_index.is | 97 |
| abstract_inverted_index.of | 10, 32, 57, 83, 169, 212 |
| abstract_inverted_index.to | 18, 44, 91, 118, 237 |
| abstract_inverted_index.HSI | 36, 239 |
| abstract_inverted_index.One | 96 |
| abstract_inverted_index.The | 105, 139, 183 |
| abstract_inverted_index.and | 69, 78, 115, 126, 145, 165, 171, 181, 190, 215 |
| abstract_inverted_index.are | 15, 42 |
| abstract_inverted_index.can | 22, 72 |
| abstract_inverted_index.for | 3, 54, 67, 74, 123, 233 |
| abstract_inverted_index.not | 16 |
| abstract_inverted_index.pig | 146 |
| abstract_inverted_index.the | 33, 46, 75, 93, 153, 163, 175, 196, 205, 217 |
| abstract_inverted_index.two | 63 |
| abstract_inverted_index.use | 87 |
| abstract_inverted_index.0.93 | 180 |
| abstract_inverted_index.0.98 | 189 |
| abstract_inverted_index.Both | 85 |
| abstract_inverted_index.PpIX | 151, 167, 220 |
| abstract_inverted_index.This | 20 |
| abstract_inverted_index.data | 148 |
| abstract_inverted_index.deep | 64, 235 |
| abstract_inverted_index.more | 80, 201 |
| abstract_inverted_index.much | 31 |
| abstract_inverted_index.must | 51 |
| abstract_inverted_index.only | 179 |
| abstract_inverted_index.over | 222 |
| abstract_inverted_index.show | 231 |
| abstract_inverted_index.than | 204 |
| abstract_inverted_index.that | 14, 41, 50, 224 |
| abstract_inverted_index.then | 116 |
| abstract_inverted_index.uses | 37 |
| abstract_inverted_index.were | 141, 188 |
| abstract_inverted_index.with | 99, 149 |
| abstract_inverted_index.(HSI) | 2 |
| abstract_inverted_index.0.82. | 182 |
| abstract_inverted_index.0.91, | 191 |
| abstract_inverted_index.0.997 | 170 |
| abstract_inverted_index.These | 229 |
| abstract_inverted_index.brain | 5, 24, 147 |
| abstract_inverted_index.data, | 195 |
| abstract_inverted_index.first | 110 |
| abstract_inverted_index.gives | 199 |
| abstract_inverted_index.human | 194 |
| abstract_inverted_index.known | 150, 164 |
| abstract_inverted_index.model | 155, 198 |
| abstract_inverted_index.other | 106 |
| abstract_inverted_index.spots | 211 |
| abstract_inverted_index.tumor | 6, 25 |
| abstract_inverted_index.using | 129, 234 |
| abstract_inverted_index.which | 71 |
| abstract_inverted_index.(PpIX) | 102 |
| abstract_inverted_index.0.990, | 172 |
| abstract_inverted_index.better | 208 |
| abstract_inverted_index.bright | 210 |
| abstract_inverted_index.linear | 39 |
| abstract_inverted_index.models | 66, 86, 140 |
| abstract_inverted_index.should | 225 |
| abstract_inverted_index.unable | 43 |
| abstract_inverted_index.values | 187 |
| abstract_inverted_index.Imaging | 1 |
| abstract_inverted_index.Pearson | 157 |
| abstract_inverted_index.account | 73 |
| abstract_inverted_index.against | 143 |
| abstract_inverted_index.between | 12, 162 |
| abstract_inverted_index.capture | 45 |
| abstract_inverted_index.correct | 119 |
| abstract_inverted_index.effects | 77 |
| abstract_inverted_index.enables | 8 |
| abstract_inverted_index.humans. | 19 |
| abstract_inverted_index.improve | 238 |
| abstract_inverted_index.labels. | 104 |
| abstract_inverted_index.manner. | 138 |
| abstract_inverted_index.method, | 207 |
| abstract_inverted_index.methods | 40 |
| abstract_inverted_index.modeled | 53 |
| abstract_inverted_index.optical | 125 |
| abstract_inverted_index.patient | 28 |
| abstract_inverted_index.phantom | 144 |
| abstract_inverted_index.process | 92 |
| abstract_inverted_index.produce | 79 |
| abstract_inverted_index.promise | 232 |
| abstract_inverted_index.propose | 62 |
| abstract_inverted_index.results | 203, 230 |
| abstract_inverted_index.spectra | 122 |
| abstract_inverted_index.tissues | 13 |
| abstract_inverted_index.trained | 98 |
| abstract_inverted_index.values) | 161 |
| abstract_inverted_index.whereas | 174 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.accurate | 55, 81 |
| abstract_inverted_index.achieved | 156, 178 |
| abstract_inverted_index.approach | 177 |
| abstract_inverted_index.biopsies | 223 |
| abstract_inverted_index.captured | 94 |
| abstract_inverted_index.computed | 166 |
| abstract_inverted_index.emission | 121 |
| abstract_inverted_index.few-shot | 137 |
| abstract_inverted_index.learning | 65, 111, 117, 236 |
| abstract_inverted_index.maximize | 23 |
| abstract_inverted_index.recovery | 56 |
| abstract_inverted_index.reducing | 216 |
| abstract_inverted_index.removing | 209 |
| abstract_inverted_index.spectra. | 95 |
| abstract_inverted_index.spectrum | 134 |
| abstract_inverted_index.specular | 213 |
| abstract_inverted_index.unmixing | 113 |
| abstract_inverted_index.variance | 218 |
| abstract_inverted_index.abundance | 221 |
| abstract_inverted_index.classical | 176, 206 |
| abstract_inverted_index.estimates | 82 |
| abstract_inverted_index.evaluated | 142 |
| abstract_inverted_index.geometric | 127 |
| abstract_inverted_index.improving | 27 |
| abstract_inverted_index.nonlinear | 76 |
| abstract_inverted_index.outcomes. | 29 |
| abstract_inverted_index.phenomena | 49 |
| abstract_inverted_index.realistic | 202 |
| abstract_inverted_index.reference | 131 |
| abstract_inverted_index.resection | 7 |
| abstract_inverted_index.therefore | 61 |
| abstract_inverted_index.training, | 109 |
| abstract_inverted_index.undergoes | 107 |
| abstract_inverted_index.unmixing, | 70 |
| abstract_inverted_index.approach's | 185 |
| abstract_inverted_index.correction | 68 |
| abstract_inverted_index.processing | 34 |
| abstract_inverted_index.properties | 128 |
| abstract_inverted_index.relatively | 227 |
| abstract_inverted_index.resection, | 26 |
| abstract_inverted_index.simplified | 38 |
| abstract_inverted_index.supervised | 154 |
| abstract_inverted_index.abundances. | 59, 84 |
| abstract_inverted_index.correlation | 158 |
| abstract_inverted_index.differences | 11 |
| abstract_inverted_index.fluorophore | 58 |
| abstract_inverted_index.non-linear, | 47 |
| abstract_inverted_index.reflectance | 133, 214 |
| abstract_inverted_index.white-light | 132 |
| abstract_inverted_index.architecture | 90 |
| abstract_inverted_index.augmentation | 21 |
| abstract_inverted_index.coefficients | 159 |
| abstract_inverted_index.fluorescence | 120 |
| abstract_inverted_index.homogeneous. | 228 |
| abstract_inverted_index.Hyperspectral | 0 |
| abstract_inverted_index.concentration | 103 |
| abstract_inverted_index.heterogeneous | 124 |
| abstract_inverted_index.hyperspectral | 112 |
| abstract_inverted_index.neurosurgery. | 242 |
| abstract_inverted_index.qualitatively | 200 |
| abstract_inverted_index.respectively, | 173 |
| abstract_inverted_index.respectively. | 192 |
| abstract_inverted_index.visualization | 9 |
| abstract_inverted_index.concentration; | 152 |
| abstract_inverted_index.concentrations | 168 |
| abstract_inverted_index.protoporphyrin | 100 |
| abstract_inverted_index.distinguishable | 17 |
| abstract_inverted_index.self-supervised | 114 |
| abstract_inverted_index.semi-supervised | 108, 184, 197 |
| abstract_inverted_index.autoencoder-like | 89 |
| abstract_inverted_index.fluorescence-guided | 4, 241 |
| abstract_inverted_index.wavelength-dependent | 48 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |