Microwave Imaging for Breast Cancer Detection: Performance Assessment of a Next-Generation Transmission System
Bibliographic record
Abstract
Microwave imaging has been proposed for breast cancer detection and treatment monitoring. Prototype systems based on tomography and radar-based techniques have been tested on human subjects with promising results. Previously, we developed a system that estimated average permittivity in regions of the breast using signals transmitted through the tissues. Encouraging results with volunteers and patients motivated development of a system capable of creating more detailed images of the entire breast. OBJECTIVE: In this paper, we aim to assess the performance of this next generation microwave imaging system and demonstrate scans of human subjects that relate to clinical information. METHODS: With a novel imaging system, scans of homogeneous phantoms and phantoms with inclusions of various sizes are collected. The accuracy, detection and localization are assessed. A pilot study is carried out with a small group of volunteers with previous mammograms. RESULTS: Images of flexible phantoms have average error of less than 10 % in the reconstructed average permittivity. Detection of inclusions of 1 cm diameter and greater is demonstrated. The feasibility of scanning human subjects is also demonstrated by providing microwave images of several healthy volunteers with previous mammograms. SIGNIFICANCE: A novel high-resolution microwave transmission imaging system, in conjunction with a fast quantitative reconstruction algorithm capable of detecting 1 cm diameter inclusions, is designed for breast imaging applications. It can image various breast sizes without the need for matching fluid. CONCLUSION: Overall, the results indicate that this imaging system is well suited for further exploration of microwave imaging with human subjects.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".