Imaging-based classification algorithms on clinical trial data with injected tumour responses
Bibliographic record
Abstract
Abstract—Current microwave breast cancer imaging algo-rithms focus primarily on generating an image, and provide little machinery for interpretation of the image. Within-image contrast is commonly used as a performance metric, but a better reflection of the tumour detection capability of an algorithm is the difference between the maximum voxel intensities observed in images from scans of tumour-free and tumour-bearing breasts. This paper extends existing imaging algorithms by incorpo-rating an automatic tumour detection technique that involves classification based on maximum voxel intensities. We compare results obtained from different algorithms on the data collected from healthy breast scans performed during clinical trials of a microwave radar system. We artificially inject tumour signals that are constructed based on the transmission properties of the radar system and the estimated breast tissue properties. The results provide insights into which algorithms are sufficiently robust to handle discrepancies between the real measurement data and the modeling assumptions. Index Terms—microwave breast cancer detection, clinical trial, imaging algorithms. I.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".