AMDI — Indexed Atlas of Digital Mammograms that Integrates Case Studies, E-Learning, and Research Systems via the Web
Bibliographic record
Abstract
Mammography is used in screening for the early detection of breast cancer in asymptomatic women. The Alberta Cancer Board (Canada) has been operating Screen Test: Alberta Program for the Early Detection of Breast Cancer since 1990. The program attracts the participation of about 21,000 women per year. In order for screening to be cost effective, means need to be developed to achieve high diagnostic accuracy. Mammograms are difficult images to interpret, especially in the screening context. Ambiguous cases with suspicious features detected on mammograms are evaluated further with adjunctive imaging procedures, such as supplementary views, ultrasonography, magnification mammography, and magnetic resonance imaging, depending on the characteristics of the abnormality. Biopsy is recommended if the imaging methods do not lead to a definite diagnosis but indicate a high suspicion for malignancy, or for confirmation of malignancy. Objective methods for the analysis of mammographic features are needed for the development of computer-aided methods to assist radiologists in the evaluation of ambiguous features. Current research is directed toward the development of digital imaging and image-analysis systems that can detect mammographic features, classify them, and give visual prompts to the radiologist.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.122 | 0.049 |
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".