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Microwave and ultrasound imaging for biomedical tissue identification

2014· article· en· W1966637872 on OpenAlexaff
Pedram Mojabi, Joe LoVetri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingInverse scattering problemIterative reconstructionAcousticsTomographyPhysicsUltrasoundOpticsInverse problemMicrowaveUltrasonic sensorScatteringPermittivityMedical imagingMaterials scienceComputer scienceDielectricMathematicsComputer visionArtificial intelligenceOptoelectronicsMathematical analysis

Abstract

fetched live from OpenAlex

Microwave tomography (MWT) and ultrasound tomography (UT) are two biomedical imaging modalities which are currently being investigated for applications such as breast cancer imaging. In MWT, the object of interest (OI) is surrounded by a number of antennas that are used to radiate the OI successively with electromagnetic waves in the microwave frequency range. The scattered electric fields are collected at receiver locations surrounding the OI. On the other hand, in UT the OI is surrounded by several ultrasound transducers illuminating the OI by acoustic waves and the scattered pressure from the OI is then collected at the receivers. In both MWT and UT, the scattered data is given to an inverse scattering algorithm to reconstruct specific properties of the object: in MWT the relative permittivity and conductivity (or complex permittivity) of the OI are reconstructed, whereas in UT the complex compressibility and inverse density are reconstructed. Both are quantitative imaging methods and thereby provide some insight into type of tissue corresponding to a pixel value in the image.

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: none
Teacher disagreement score0.911
Threshold uncertainty score0.329

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 head0.004
GPT teacher head0.215
Teacher spread0.211 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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