Microwave Imaging for Monitoring Changes: Improvements to Images and Application of Analysis Tools
Bibliographic record
Abstract
Microwave imaging is of increasing interest for breast imaging because it is inexpensive, safe, and fast. Previously, tumour detection has been the focus of microwave imaging re- search, however, breast cancer treatment monitoring is a growing area of interest. Microwave imaging is especially well suited for frequent scanning because it does not expose patients to harmful radiation. Currently, there are no clinical imaging methods routinely used to monitor breast tissue during radiation treatment or shortly after. As a result, early indicators of recurrence or fibrosis (the scarring and thickening of tissue) are not measured. In this thesis, we investigate a microwave transmission system developed at the University of Calgary for the application of monitoring breast changes over time. This imaging system scans a region of the breast and creates low resolution images showing the distribution of tissue. Despite the low resolution, the system demonstrates consistency when scanning over time. In this work, the causes for variation between scans and the performance of the system when scanning frequently are further investigated. Furthermore, microwave scans are evaluated for a group of cancer patients undergoing radiation treatment, and changes between treated and untreated breast over time are identified. Finally, a preliminary alignment method between microwave and mammogram images is introduced. Using this method, the features from mammography are related to features in the microwave scans. This thesis provides results that suggest the microwave transmission system can detect breast differences that are consistent with the clinical history of patients.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".