An Indexed Atlas of Digital Mammograms for Computer-Aided Diagnosis of Breast Cancer
Bibliographic record
Abstract
Screening mammography is used for the early detection of breast cancer in asymptomatic women (between the ages of 50 and 69 in Canada). Screen Test: Alberta Program for the Early Detection of Breast Cancer has been in operation since 1990, and attracts the participation of over 21,000 women per year. Screen Test has offices in Calgary and Edmonton, and offers extended service with three mobile units to more than 100 sites in the province of Alberta. Mammograms are difficult images to interpret, especially in the screening context where the probability of encountering an abnormality is low and patient information is limited. A cost-effective, efficient method needs to be developed in order to achieve high diagnostic accuracy. Diagnostic mammography refers to the radiological examination of symptomatic women who exhibit clinical signs such as a palpable lump, skin puckering, or nipple retraction, or as a result of screening. Mammography is used to detect abnormalities and classify them as benign or malignant. Ambiguous cases with suspicious features detected on mammograms are evaluated further with adjunctive imaging procedures. Depending on the characteristics of the abnormality, these procedures may include supplementary views, ultrasound, magnification mammography, magnetic resonance imaging, computed tomography, and nuclear medicine techniques. Biopsy is indicated if these methods do not lead to a definite diagnosis but indicate a high suspicion for malignancy, and confirmation of malignancy is required. Objective methods for the analysis of mammographic features are needed for the development of computer methods to assist radiologists in the evaluation of ambiguous features; that is, for computer-aided diagnosis (CAD) of breast cancer. Current research is directed toward the development of digital mammographic imaging and image analysis systems that can detect features, classify them, and give visual prompts to the radiologist, such as the Image Checker by R2 Technology, EasyVision RAD used with Philips' Computed Radiography system, and Second Look from iCAD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.157 | 0.083 |
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".