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Record W4323306664 · doi:10.1117/12.2650504

Multipath contrast of optical coherence tomography for early cancer detection (Conference Presentation)

2023· article· en· W4323306664 on OpenAlexaff
Jeanie Malone, A. Tanskanen, Lien Hoang, Jessica N. McAlpine, Calum MacAulay, Pierre Lane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSimon Fraser UniversityVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsOptical coherence tomographyMultipath propagationComputer scienceImage resolutionOpticsOptical tomographyArtificial intelligencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

We hypothesize that there may be cancer-sensitive image biomarkers present in a novel image processing technique. We leverage the multipath artifacts derived from higher order modes that present in double clad fiber-based OCT systems, which are sensitive to scattering angle and as such may be sensitive to sub-resolution features such as nuclear density and size. This work explores multipath contrast in previously collected clinical imaging data; preliminary work has found that this technique can distinguish cancerous and non-cancerous fallopian tube specimens.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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