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Record W2169618547 · doi:10.1109/tmtt.2006.871224

Tissue sensing adaptive Radar for breast cancer detection-investigations of an improved skin-sensing method

2006· article· en· W2169618547 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 Microwave Theory and Techniques · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicrowave imagingRadarDeconvolutionImpulse responseComputer scienceMicrowaveAcousticsMathematicsPhysicsTelecommunicationsAlgorithm

Abstract

fetched live from OpenAlex

Active microwave breast imaging is being researched as a supplement to current breast imaging modalities. Ultra-wideband radar approaches involve analyzing reflections from the breast to identify the presence of tumors. Skin sensing, which involves estimating the location and thickness of the skin, is a key step in this process, as the reflections from the skin dominate the signal. Current methods employing a rudimentary peak detection process estimate the location of the breast with acceptable accuracy. However, estimates of skin thickness in the range of 1.0-2.0 mm have unacceptable error. A method using deconvolution to obtain the impulse response of a scattering object is investigated to improve the performance of the skin-sensing algorithm. The new method employs a calibration step using a perfect electric conductor. Application to simulated data shows success in reducing the error percentage in both breast skin location and thickness estimates by more than half.

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: Methods · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score0.974

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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