Microwave Imaging for Breast Cancer Detection: Performance Assessment of a Next-Generation Transmission System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it