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 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 | 0.001 | 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".