On the Potential Use of Anatomical and Epidemiological Information to Enhance Microwave and Ultrasound Breast Imaging
Bibliographic record
Abstract
This paper proposes the use of available anatomical and epidemiological (statistical) information regarding breast cancer in conjunction with microwave and ultrasound breast imaging. This information, which can be extracted from the broader medical research effort, can be used in conjunction with image reconstruction algorithms to further guide those algorithms towards a more accurate solution. In particular, we propose to consider the following anatomical and statistical information into the image reconstruction process via the tissue-type framework. Anatomical information is utilized in assigning a tissue-type and its corresponding probability to a given pixel by considering the neighbouring reconstructed pixels (epidemiological information related to the anatomical surroundings of the pixel is used to modify the prior probabilities in the Bayes prediction model). Two types of epidemiological information are used in reconstructing the tissue-type image to improve the discrimination between tumor and cyst: the quadrant of the breast within which a pixel is located provides statistical information regrading whether of being cancerous, and the age of the patient can provide similar information.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".