Microwave time-domain radar for monitoring breast health: Miniaturized hardware and anomaly detection algorithms
Bibliographic record
Abstract
Breast screening is the most effective means for detecting the presence of breast cancer at an early stage. The existing modalities cannot be used frequently due to constraints posed by their operating principles. Thus, microwave imaging has been proposed as a viable complimentary technique which can be used for performing frequent and comfortable breast scans. The Breast Cancer Detection Research Group at McGill University has developed a tabletop time-domain system which has undergone early clinical trials. The group intends to develop a device which encompasses its components into a conformal bra. The currently used clock generator and pulse generator are the Tektronix gigaBERT 1400 Clock Generator and the Picosecond Pulse Labs Model 3600 respectively. These are large and expensive components. Thus, the combination of the Adafruit Si5351 Clock Generator and the Furaxa development board is proposed as a viable alternative to significantly reduce the size and the cost of the system. The proposed miniaturized components are characterized and shown to be viable replacements to the currently used devices. Anomaly detection algorithms are a family of techniques which intend to define a nominal set and detect examples which differ from this normality. Anomaly detection algorithms are proposed as a means of detecting the presence of malignant tissues within the breast. The proposed algorithms are compared using a dataset collected from a single volunteer across a 28-day period. Through the work presented in this thesis, it is our intention to bring the Breast Cancer Detection Group closer to producing a low-cost conformal breast health monitoring device.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".