Unsupervised time reversal based microwave imaging for breast cancer detection
Bibliographic record
Abstract
Microwave breast imaging is performed by illuminating the breast tissues with a short pulse of microwaves and processing the reflections (backscatter) to create a pseudospectrum that detects the presence of the breast tumours specifying their locations. An important step in such breast cancer detection techniques is the backscatter pre-conditioning step for effective suppression of the clutter signals arising from scattering mechanisms other than the tumor including the antenna reverberations and reflections from the skin-breast interface and chest wall. The paper proposes a new clutter suppression algorithm that successfully isolates the tumour response from the overall (tumour and clutter) response. The proposed DAF/EDF approach is based on a combination of the data adaptive filter (DAF) and the envelope detection filter (EDF), and does not require any prior training. The DAF/EDF algorithm is then coupled with the time reversal (TR) array imaging approaches [1, 2, 3] and tested by running finite difference, time difference (FDTD) electromagnetic simulations based on the magnetic resonance imaging (MRI) data of the human breast. Our results demonstrate the effectiveness of the DAF/EDF algorithm for microwave breast cancer detection.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".