Tissue Sensing Adaptive Radar for Breast Tumour Detection: Investigation of Issues for System Implementation
Bibliographic record
Abstract
Microwave imaging methods for breast cancer detection have gained the attention of many researchers. These methods aim to detect tumours by exploiting the differences in electrical properties between healthy tissues and malignancies. One group of methods is radar-based, and involves illuminating the breast with an ultra-wideband signal, observing reflections, then isolating and focusing reflections from tumours. One radar-based method is tissue sensing adaptive radar (TSAR). This technique senses all tissues in the region of interest, and adapts the imaging algorithm accordingly. This paper explores several issues related to practical implementation. First, an appropriate immersion liquid is selected. The breast and antenna are placed in this liquid, which must be safe, easy to implement and provide reasonable imaging capabilities. Second, the skin-sensing step is improved in order to provide reliable estimates of both the location and thickness of the skin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".