Tissue sensing adaptive radar for breast cancer detection: investigations of reflections from the skin
Bibliographic record
Abstract
Alternative methods of breast cancer detection are of interest due to the limitations of the current gold standard imaging method, mammography (S.J. Nass et al., eds., Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer, National Research Council, 2001.). One method of radar-based breast imaging is tissue sensing adaptive radar (TSAR), which senses all tissues in the volume of interest and adapts the algorithm accordingly (E.C. Fear and M. Okoniewski, Microwave and RF Appl., American Ceramic Soc., pp. 487-494, 2003). The dominant reflection in the initial TSAR signal is due to the skin surrounding the breast, which is electrically different from normal breast tissues. This reflection contains information such as the location of the object of interest, and depends on the skin thickness and electrical properties. By analysing the skin reflection, we may extract more information to use in the TSAR algorithm. For example, the location information is used by the TSAR algorithms to create an outline of the region of interest. The thickness information and electrical properties are used in image formation. In this paper, we investigate the application of deconvolution techniques to the initial TSAR signals. The aim of deconvolution in this case is to provide improved estimates of both skin location and thickness. Improved estimates of both of these quantities are expected to result in improved TSAR images.
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 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".