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Record W2031220720 · doi:10.1109/tbme.2012.2209116

Toward Morphological Thoracic EIT: Major Signal Sources Correspond to Respective Organ Locations in CT

2012· article· en· W2031220720 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsCarleton University
Fundersnot available
KeywordsElectrical impedance tomographyIterative reconstructionTomographyComputer visionArtificial intelligenceComputer scienceSIGNAL (programming language)Boundary (topology)Data setMedical imagingPattern recognition (psychology)Biomedical engineeringPhysicsMathematicsEngineeringOptics

Abstract

fetched live from OpenAlex

Lung and cardiovascular monitoring applications of electrical impedance tomography (EIT) require localization of relevant functional structures or organs of interest within the reconstructed images. We describe an algorithm for automatic detection of heart and lung regions in a time series of EIT images. Using EIT reconstruction based on anatomical models, candidate regions are identified in the frequency domain and image-based classification techniques applied. The algorithm was validated on a set of simultaneously recorded EIT and CT data in pigs. In all cases, identified regions in EIT images corresponded to those manually segmented in the matched CT image. Results demonstrate the ability of EIT technology to reconstruct relevant impedance changes at their anatomical locations, provided that information about the thoracic boundary shape (and electrode positions) are used for reconstruction.

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.

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: none
Teacher disagreement score0.602
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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