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Record W2085894101 · doi:10.1109/sopo.2012.6271131

Multimodal Optical Tissue Imaging

2012· article· en· W2085894101 on OpenAlexaff
Shuo Tang, Yifeng Zhou, Tom Lai, Myeong Jin Ju

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOptical coherence tomographyOptical imagingMedical imagingComputer scienceFluorescence-lifetime imaging microscopyMicroscopyOpticsBiological imagingMultiphoton fluorescence microscopeBiomedical engineeringFluorescenceArtificial intelligenceFluorescence microscopePhysicsMedicine

Abstract

fetched live from OpenAlex

This paper presents tissue imaging with a multimodal multiphoton microscopy (MPM) and optical coherence tomography (OCT) system. The multimodal system can acquire multiple contrasts including two-photon excited fluorescence, second harmonic generation, and scattering. The system can also acquire tissue level imaging with OCT and cellular level imaging with MPM. The MPM/OCT imaging is demonstrated on onion skin and cornea samples. The multimodal MPM/OCT provides a multi-contrast and multi-field-of-view imaging system which has potential applications in cancer detection and diagnosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.243
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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