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Record W4318154284 · doi:10.1117/12.2668370

Raman macroscopic imaging system for intraoperative brain cancer detection

2023· article· en· W4318154284 on OpenAlexaff
Patrick Orsini, Joannie Desroches, François Daoust, Hugo Tavera, Frédéric Dallaire, Kevin Savard, Jacques Bismuth, Philippe Mckoy, Israël Veilleux, Kevin Petrecca, Frédéric Leblond

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsMcGill UniversityPolytechnique MontréalUniversité de MontréalOptech (Canada)
Fundersnot available
KeywordsRaman spectroscopyBiomedical engineeringCancer detectionMaterials scienceOpticsMedical imagingComputer scienceNuclear magnetic resonanceArtificial intelligenceMedicinePhysicsCancer

Abstract

fetched live from OpenAlex

We present a macroscopic line scanning Raman imaging system which has been modified to be suitable for intraoperative use. A sterilizable probe muzzle was designed to flatten the biological tissue ensuring its position at the focal plane of the Raman probe optics, removing the need for probe sterilization. The system uses a flexible imaging probe with a 1cm2 field of view to record fingerprint Raman images, mounted on an articulated arm that supports the probe weight and allows gentle contact with the tissue. Validation results obtained on porcine tissues show >95% classification accuracy between different tissue types.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.011
GPT teacher head0.361
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), 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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