Mammographic information analysis through association-rule mining
Bibliographic record
Abstract
The increasing availability of large clinical and biomedical data repositories provides researchers with substantial opportunities for data analysis and knowledge discovery. Data mining is an expanding research frontier that provides numerous efficient and scalable methods to extract patterns of interest in datasets. The University of Calgary Atlas of Mammograms (U of C Atlas) contains digital mammographic images and textual reports of radiologists acquired from Screen Test Alberta. Many advanced image-processing techniques have been applied to the images in this dataset. However, research has not been conducted to take advantage of data-mining techniques, which motivates us to investigate the functionality of association-rule mining techniques to discover patterns of interest in the existing dataset. This paper describes preliminary results of the application of applying association-rule mining techniques to the U of C Atlas. We propose a new breast mass classification method based on quantitative association-rule mining. The experiments conducted on the U of C Atlas show that many interesting rules can be generated from this dataset, and indicate previously unobserved patterns in the information contained in the atlas.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 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".