Multiple Multi-Modal Methods of Malignant Mammogram Classification
Bibliographic record
Abstract
Breast cancer is a tragic disease, which affects approximately 1 out of 8 women. Mammograms are X-ray scans used to detect early breast cancers, which are often susceptible to human error. As such, a strong emphasis has been placed on creating novel techniques to detect early cancers in mammogram scans. Analyzing and detecting breast cancer can be considered as a multi-modal task, which combines patient health information with their scan data. In this paper, we present a healthcare informatics solution-which consists of multiple multi-modal classification methods-and apply these methods in our healthcare informatics solution detect cancers in the Radiological Society of North America (RSNA) Breast Cancer Dataset. In this work, we perform preprocessing and describe novel architectures to classify raw mammogram scan data. Our evaluation results indicate that our destructive patching and embedding concatenation methods lead to relatively high accuracy scores and significantly faster convergence. Additionally, we compare and validate multiple methods for aggregating predictions made on multiple scans to determine whether a patient has cancer. Ultimately, we achieve a balanced accuracy of 70.2% on this task of classifying mammograms into benign (i.e., not harmful) or malignant (i.e., very virulent).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".