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Record W2169287185 · doi:10.1109/tbme.2010.2077637

Real-Time Multispectral Imager for Home-Based Health Care

2010· article· en· W2169287185 on OpenAlexaff
Dingrong Yi, Chao Wang, Hairong Qi, Linghua Kong, Fengtao Wang, Ali Adibi

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2010
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and Bioengineering
KeywordsMultispectral imageComputer scienceMobile deviceSignature (topology)Computer visionArtificial intelligenceReal-time computing

Abstract

fetched live from OpenAlex

Multispectral imaging (MSI) is becoming a powerful tool for tissue abnormality detection. Conventional MSI systems, however, are not readily suitable for challenges of routine clinical uses due to the fact that they are expensive, bulky, and time consuming to acquire the data. In this letter we report a novel approach to instrument MSI technology into a handheld, low-cost, standing-alone, real-time operational device that is suitable for home-based health care. It covers techniques used to produce multiple images at discrete signature wavelengths of tissues with a single shot.

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: Methods · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score0.735

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 integrity0.0000.001
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 head0.006
GPT teacher head0.286
Teacher spread0.280 · 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
GenreMethods

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

Citations11
Published2010
Admission routes1
Has abstractyes

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