Initial design and simulation of a portable breast microwave detection device
Bibliographic record
Abstract
Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death for women in Canada. Access to breast cancer screening is limited in northern and First Nation communities in Canada, as well as low- and middle-income countries (LMIC) in the broader international community. Lack of accessible screening tools contribute to inequitable care and increased mortality rates. Microwave breast detection has shown promising results as a safe, low-cost breast cancer screening method. This thesis aims to design and simulate a portable microwave device suitable for use in remote and low-income areas. The device features a cylindrical array of patch antennas and, through machine learning, will require no trained personnel to operate or obtain a preliminary diagnosis. Tiny vector network analyzers were used to measure S-parameters from 0.7 - 3 GHz. A basic radar model was improved to incorporate spherical spreading, microwave attenuation, and secondary scattering. This radar model was used to simulate time-domain sinograms of dual rod models. The rod models consisted of two point scatterers with assigned reflectivities of 100% & m% or m% & m%, where m was varied as: 10%, 30%, 50%, 70%, and 90%. The simulated sinograms were used to train a convolutional neural network to locate the rod responses. The network was able to easily detect and locate the 100% rod response, but struggled with the lower 10% and 30% reflectivities. The 100% and 90% rod models performed with an accuracy of 92%. The use of machine learning improved upon conventional reconstruction methods for breast microwave radar. Similar machine learning techniques can be performed on real scans with shell-based breast phantoms. The radar model simulations can be used to increase the data set size and improve training performance through transfer learning. Microwave sensing and machine learning methods offer promising possibilities to breast cancer screening accessibility for women in remote and low-income areas.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".