Improved Tissue Mimicking Materials for Shell-Based Phantoms in Breast Microwave Sensing
Bibliographic record
Abstract
Breast microwave sensing has shown encouraging results as a method for breast cancer detection. Accurate breast phantoms are required to test and optimize this method before clinical trials, and several types of phantoms have been proposed. No single phantom type that meets all the requirements has yet been established. MRI-derived 3D-printed shells can be used to represent the breast morphology. These versatile shells are filled with tissue-mimicking liquids that simulate the dielectric properties of breast tissues in the desired frequency range. This work investigates the dielectric properties of various solid and liquid materials from 0.4-9.0 GHz. The solid materials were fabricated to mimic adipose tissue, and the liquid mixtures were prepared to simulate adipose and fibroglandular tissue. Carbon-black, graphite, and resin mixtures improved the properties used in current shell-based phantoms. The mean absolute percentage error (MAPE) between the solid materials and target adipose permittivity was 11.2%. Liquid solutions of DGBE and water were found to improve adipose and fibroglandular tissue-mimicking liquids with a MAPE of 13.3% and 5.8%, respectively.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".