Hagness, “Development of anatomically realistic numerical breast phantoms with accurate dielectric properties for modeling microwave 632
Bibliographic record
Abstract
Abstract—Computational electromagnetics models of mi-crowave interactions with the human breast serve as an invaluable tool for exploring the feasibility of new technologies and improv-ing design concepts related to microwave breast cancer detection and treatment. In this paper, we report the development of a col-lection of anatomically realistic 3-D numerical breast phantoms of varying shape, size, and radiographic density which can readily be used in finite-difference time-domain computational electro-magnetics models. The phantoms are derived from T1-weighted MRIs of prone patients. Each MRI is transformed into a uniform grid of dielectric properties using several steps. First, the struc-ture of each phantom is identified by applying image processing techniques to the MRI. Next, the voxel intensities of the MRI are converted to frequency-dependent and tissue-dependent dielectric properties of normal breast tissues via a piecewise-linear map. The dielectric properties of normal breast tissue are taken from the recently completed large-scale experimental study of normal breast tissue dielectric properties conducted by the Universities of Wisconsin and Calgary. The comprehensive collection of numeri-cal phantoms is made available to the scientific community through an online repository. Index Terms—Biomedical applications of electromagnetic radia-tion, biomedical electromagnetic imaging, breast cancer detection, breast cancer treatment, finite-difference time-domain (FDTD) methods, microwave hyperthermia, microwave imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".