A Quantitative Analysis of the Impact of Glass as a Phantom Shell Material in Breast Microwave Sensing
Bibliographic record
Abstract
Shell-based breast phantoms use shells to contain liquids that act as breast tissue surrogates by mimicking the dielectric properties of breast tissues. These phantoms are advantageous for their ease of use, robustness, and experimental utility. The use of liquids as the tissue mimicking material allows the user to create and readily modify liquid solutions to model the desired dielectric properties, the free positioning of tumour surrogates inside healthy tissues, and experimental reproducibility and transparency when 3D-printed plastic shells are used. The primary disadvantage of shell-based phantoms is their use of (typically low permittivity) shell materials to hold the tissue-mimicking liquids. This work investigates the impact of a 0.66 mm thick glass shell, used to contain a tumour-mimicking solution, on the measured S11response in a pre-clinical breast microwave sensing system. A statistical analysis of the results obtained in this work indicate that the presence of the glass shell produces a response that cannot be detected by the imaging system, and therefore is suitable for use in shell-based phantoms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".