Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models
Bibliographic record
Abstract
Microwave breast cancer detection is based on differences in electrical properties between healthy and malignant tissues. Tissue sensing adaptive radar (TSAR) has been proposed as a method of microwave breast imaging for early tumor detection. TSAR senses all tissues in the volume of interest and adapts accordingly. Simulation results have shown the feasibility of this system for detecting tumors of 4 mm in diameter. In this paper, the second-generation experimental system for TSAR is presented. Materials with electrical properties similar to those in the breast are used for the breast model. A resistively loaded Wu–King monopole antenna is fabricated, and reflections from the breast model over the frequency range of 1–10 GHz are recorded. The reflected signals are processed with the TSAR algorithm, which includes improved skin subtraction and TSAR focusing algorithms. Various tumor models are examined; specifically, a 1-cm tumor is detected with a signal-to-clutter ratio of 10.41 dB. Tumor detection with the experimental system is evaluated and compared to simulation results.
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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 | 0.000 | 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".