Demonstrating the Utility of a Pipeline for Microwave Breast Image Analysis: A Two-Factor Variation Study
Bibliographic record
Abstract
Microwave imaging (MWI) is a promising non-invasive technique for breast cancer screening, utilizing differences in tissue dielectric properties to produce images. This study demonstrates the potential of a pipeline that enables quantitative, high-throughput analysis of reconstructed images. Microwave measurements were simulated in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1-5 \text{GHz}$</tex> frequency range using a simplified breast model, which includes a skin layer encasing homogeneous healthy tissue, with a tumor target embedded in the background. An 8 -antenna array was used to simulate data acquisition while the dielectric properties of the background as well as the tumor location were simultaneously and randomly varied. To conduct the simulation, models were constructed in HFSS, and computations were performed on Compute Canada's supercomputer, enabling efficient processing within a short timeframe. Image reconstruction was achieved using the open-source delay-and-sum (DAS) radar-based imaging algorithm MERIT. Metrics such as signal-to-noise ratio (SNR) and location error were applied to evaluate image quality. Preliminary results demonstrate the robustness and accuracy of the pipeline, successfully reconstructing tumors across ten scenarios with varying background permittivity and tumor locations. The findings underscore the importance of accurate tissue property modeling, as adjusting the input permittivity to account for the skin layer significantly improved image quality. Additionally, we highlight the effect of tumor position on reconstruction accuracy. In conclusion, this work provides valuable insights into the evaluation of MWI systems, introducing tools and metrics for testing performance and robustness in unknown scenarios.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".