Time-Domain Microwave Breast Cancer Detection: Extensive System Testing with Phantoms
Bibliographic record
Abstract
Early detection of breast cancer is known to be a key factor in the successful treatment of the disease. Here, we present a detection technique complementary to the currently used modalities (primarily mammography, ultrasound and magnetic resonance imaging). Our time-domain breast cancer detection system transmits microwave-range pulses into the breast and records the scattering off of the breast in order to detect malignancies. This method is made possible by an intrinsic contrast in the dielectric parameters, specifically the relative permittivity and conductivity, of the healthy and malignant breast tissues over the microwave frequency range. The long-term goal of our work is to develop a system that can be used periodically to monitor for unusual changes in breast tissues; for instance, healthy breasts would be scanned, and follow-up scans at regular intervals would detect any small changes in breast tissue composition that could indicate the presence of a malignant growth. At that point, the patient would be referred to see a doctor for further investigation of the abnormal results. Such a system would compare each new scan with previous ones to determine the level of tissue changes, and would be used by patients at home. We report feasibility and performance tests for our initial system, conducted with breast phantoms made up of tissue-mimicking materials (unique skin, fat, gland and tumor mixtures). We initiated the system testing with simple homogeneous phantoms, consisting solely of adipose tissue. Then, we extended our tests to cases of increasing complexity by adding a skin layer and varying percentages of glandular structures and tumor sizes. In order to optimize the experimental system, we performed tests with multiple antenna arrangements, tumor sizes and locations. This work shows that there are specific antenna arrangements that are advantageous for tumor detection and demonstrates the capabilities of our time-domain microwave breast tumor detection system.
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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 | 0.001 | 0.002 |
| 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".