A global optimization technique for microwave imaging of the inhomogeneous and dispersive breast
Bibliographic record
Abstract
This paper illustrates how breast tissue composition, modeled by different mixtures of adipose and glandular tissues, affects the accuracy of microwave breast imaging for different sizes of malignant lesions. To study this, the scattered field for different tissue composition and various tumour sizes was calculated using a Frequency Dependent Finite Difference Time Domain ((FD)2TD) approach. Images are generated from the scattered field, together with Genetic Algorithm (GA) optimization methods. The scattered field calculations show that it is strongly dependent not only on the dielectric properties and size of the breast tissue, but also on the specific tissue composition. The tumour response is the difference between scattered fields of a specific tissue composition with and without the tumour. The response of a 1.5cm tumour was found to be 6.7 times larger when it is embedded within homogeneously uniform fatty tissue than when embedded within homogeneously uniform fibro-glandular tissue. For a tumour inside a heterogeneously dense breast consisting of a mix of fi bro-glandular and fatty tissues, this value is 5.2. The consequences of the biological heterogeneity on the forward and inverse simulation, and on the accuracy of images obtained by microwave imaging using the proposed method were studied. The robustness of our approach to variations in the breast phantom was shown. This technique returned highly accurate results with millimetre resolution. The most rigorous test to date demonstrated that we are able to accurately reconstruct an image of the dielectric properties of a 7.5mm lesion embedded in fibro-glandular tissue at a depth of 6cm in a heterogeneous numerical breast phantom.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".