Breast tissue mimicking phantoms for combined ultrasound and microwave imaging
Bibliographic record
Abstract
We present a new formulation for a breast tissue-mimicking phantom for combined microwave and ultrasound imaging to assist breast cancer detection. Formulations based on coconut oil, canola oil, agar and glass beads were used to mimic skin and fat tissues. First, 36 recipes were fabricated, and properties were measured to determine the relationship and possible interaction between ingredients with the ultrasound and microwave properties. Based on these results, the formulae were developed to mimic different tissues found in breast, including skin, fat, fibroglandular, and tumour tissues. All phantoms contained a base of agar and glass beads at different proportions depending on the tissue mimicked. Tumour and fibroglandular tissues were best mimicked by adding polyvinylpyrrolidone (PVP), while using coconut oil for skin and canola oil for fat produced the best results. Five final phantoms with different internal structures were fabricated and imaged using B-mode ultrasound and a microwave transmission system. Microwave permittivity maps were obtained from the microwave system and compared to ultrasound images. The structure and composition of the phantoms were all confirmed through this microwave and ultrasound imaging.
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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 | 0.000 |
| 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".