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Diffuse reflectance measurements using lensless CMOS imaging chip

2014· article· en· W2170591606 on OpenAlexaff
Irina Schelkanova, Aditya Pandya, Duoaud Shah, Lothar Lilge, Alexandre Douplik

Bibliographic record

VenueJournal of Physics Conference Series · 2014
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsSt. Michael's HospitalPrincess Margaret Cancer CentreUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsImaging phantomMaterials scienceMicrofluidicsDiffuse reflectance infrared fourier transformOpticsRGB color modelWavelengthBiomedical engineeringLens (geology)ChipDiffuse reflectionOptoelectronicsNanotechnologyComputer scienceChemistryMedicinePhysicsComputer vision

Abstract

fetched live from OpenAlex

To assess superficial epithelial microcirculation, a diagnostic tool should be able to detect the heterogeneity of microvasculature, and to monitor qualitative derangement of perfusion in a diseased condition. Employing a lensless CMOS imaging chip with an RGB Bayer filter, experiments were conducted with a microfluidic platform to obtain diffuse reflectance maps. Haemoglobin (Hb) solution (160 g/l) was injected in the periodic channels (grooves) of the microfluidic phantom which were covered with ~250 μm thick layer of intralipid to obtain a diffusive environment. Image processing was performed on data acquired on the surface of the phantom to evaluate the diffuse reflectance from the subsurface periodic pattern. Thickness of the microfluidic grooves, the wavelength dependent contrast between Hb and the background, and effective periodicity of the grooves were evaluated. Results demonstrate that a lens-less CMOS camera is capable of capturing images of subsurface structures with large field of view.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.529

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)0.0000.000

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 head0.070
GPT teacher head0.344
Teacher spread0.274 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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