A Bayesian Approach for the Classification of Mammographic Masses
Bibliographic record
Abstract
Breast cancer is a major cause of deaths among women and the leading cause of death among all cancers for middle-aged women in most developed countries. Presently there are no methods to prevent breast cancer thus early detection of this disease represents a very important factor in its treatment and plays a major role in reducing mortality. Mammography is one of the most reliable methods in early detection of breast cancer. In this paper, we present a novel algorithm for medical mammogram image classification, based on the Dirichlet mixture model. Our method can be divided into three main steps: Preprocessing, feature extraction, and image classification. First, histogram equalization is used to remove the noise and to enhance the quality of the image. Later, we extract texture information from mammographic images using the Local Binary Pattern (LBP) and Haralick texture descriptor (HTD). Then, we use the Birth and Death Markov Chain Monte Carlo to estimate the parameters of the Dirichlet mixture representing each class from our training set. Finally, in the classification stage, each mammogram image is assigned to the class increasing more its likelihood. Extensive simulations are used to show the merits of our approach.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".