Neural Network Model for Breast Tissue Thickness Estimation
Bibliographic record
Abstract
This paper explores the use of optimization methods to estimate thicknesses of subcutaneous tissues using a non-invasive near-field antenna with microwave low-power reflectometry from 2-13.5 GHz. An application is in breast cancer detection at microwave frequencies. The initial model assumes a planar tissue stack and is implemented with a Long Short-Term Memory (LSTM) neural network trained on full-wave electromagnetic simulation data. Tissue phantoms for skin, fat and muscle with 2 and 3 layers are used for experimental validation. The model developed for this paper predicts thicknesses of a stackup of Playdough, Rogers Duroid 6010 and a commercial skin phantom within 7%, 41%, and 24% respectively, with a neural network loss value of 0.29. This preliminary model is a proof of concept for a higher fidelity model that can additionally predict dispersive permittivities and conductivities of layered tissues.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".