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Record W2345102747 · doi:10.1109/jmmct.2016.2560625

Composite Tissue-Type and Probability Image for Ultrasound and Microwave Tomography

2016· article· en· W2345102747 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 journal on multiscale and multiphysics computational techniques · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTomographyProperty (philosophy)UltrasoundComputer scienceComputed tomographyComputer visionArtificial intelligenceImage (mathematics)Iterative reconstructionMedical physicsRadiologyMedicine

Abstract

fetched live from OpenAlex

The concept of creating a composite tissue-type-image (cTTI) along with an associated probability image is introduced for ultrasound and microwave tomography. The cTTI integrates information available within different quantitative property images, and the associated probability image provides an indication of the level of confidence regarding the reconstructed tissue types. It is shown that the cTTI concept can be applied to ultrasound tomography property images, microwave tomography property images, as well as to their combination. Thus, the concept is generalizable to the amalgamation of quantitative information derived from a wide variety of modalities with the goal of increasing the confidence in the reconstructed cTTI. Validation of the concept is performed on MRI-derived numerical breast phantoms containing up to five 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.

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 score0.198
Threshold uncertainty score0.624

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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