Integrating Interpretability into Deep Learning Models for Mammogram-Based Breast Cancer Detection
Bibliographic record
Abstract
Breast cancer is one of the most common cancers found in women, affecting 1 in 8 Canadian women over their lifetime, according to the Public Health Agency of Canada. The early diagnosis of breast cancer can be extremely beneficial for reducing the spread of cancer cells and the risk of death. Machine learning (ML) systems can help healthcare professionals diagnose breast cancer with better accuracy by identifying underlying patterns in the images that radiologists might find difficult to detect early on. While deep learning (DL) models can obtain high accuracies, their black-box nature inhibits their ability to explain how they came to their conclusion, creating distrust between DL systems and radiologists. This research employs two CNN model architectures - AlexNet and Houby & Yassin's CNN - to classify recently obtained mammographic images from the KAU-BCMD dataset into BI-RAD categories (0-6). To resolve the problem of non-explainability, two variants of SHapley Additive exPlanations (SHAP), an explainability approach based on Shapley values, have been used to assess the role that each feature has in the prediction process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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 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".