Imaging breast tumors with microwaves : simulation-based assessment of detection capabilities of a broadband antenna-sensor
Bibliographic record
Abstract
The work reported in this thesis is motivated by the need for new screening techniques for detecting early-stage breast tumors. In recent years, pulsed microwave imaging in the gigahertz range has been suggested as a promising complementing methodology to the currently existing detection and imaging modalities. This technique is based on significant electrical contrast between the cancerous and healthy breast tissue in the microwave range. To exploit this electrical contrast for imaging purposes, a broadband trans-receiving antenna is placed near the breast surface. The antenna launches a pulse and then collects the backscattered response, used for detection of the potentially underlying tumor. In our work, we examine tumor detection capabilities of the "Dark Eyes" antenna, recently reported in the literature and suggested as antenna of choice for pulsed microwave breast imaging due to its compact size, ease of fabrication and cost-effectiveness. The simulation tool, SEMCAD-X, is based on the finite-different time-domain method and is used throughout this work to construct the realistic hemi-spherical breast model and analyze its interaction with the microwave radiated from the antenna source.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".