Experimental Assessment of Axillary Lymph Node Microwave Tomography Using Anthropomorphic Phantoms
Bibliographic record
Abstract
We assess the application of microwave tomography (MWT) for the detection of axillary lymph nodes (ALNs) in breast cancer patients. We numerically study the effects of limiting angular view in axillary MWT, as probes can only be placed on a limited arc around the axillary region. We also numerically study the possibility of increasing the amount of retrievable information by acquiring data in two consecutive steps, with a single antenna set in two different angular positions. We finally experimentally test axillary MWT on anthropomorphic phantoms with different levels of anatomical fidelity, and different ALN positions. Our MWT system (0.8–2.5 GHz) employs six monopole antennas placed on a single transverse plane, facing the axillary region. The reconstruction algorithm implements the distorted Born iterative method, combined with the two-step iterative shrinkage/thresholding for the inversion (DBIM-TwIST). Our numerical results i) highlight the challenges associated with the limited angular view, and ii) show that performing two-step angular measurements enhances imaging results, suggesting that rotating the antenna set between consecutive measurements is an effective means to increase the retrievable information in ALN MWT. Our experimental results show that our MWT system can detect an ALN in different positions. To the best of our knowledge this is the first paper to assess ALN MWT in a realistic 3-D experimental scenario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".