Mammographic information analysis through association-rule mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it