Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography
Bibliographic record
Abstract
This study evaluates a deep learning model for classifying normal versus potentially abnormal regions of interest (ROIs) on mammography, aiming to identify imaging, pathologic, and demographic characteristics that may induce suboptimal model performance in certain patient subgroups. We utilized the EMory BrEast imaging Dataset (EMBED), containing 3.4 million mammographic images from 115,931 patients. Full-field digital mammograms from women aged 18 years or older were used to create positive and negative patches with the patches matched based on size, location, patient demographics, and imaging features. Several convolutional neural network (CNN) architectures were tested, with ResNet152V2 demonstrating the best performance. The dataset was split into training (29,144 patches), validation (9,910 patches), and testing (13,390 patches) sets. Performance metrics included accuracy, AUC, recall, precision, F1 score, false negative rate, and false positive rate. Subgroup analysis was conducted using univariate and multivariate regression models to control for confounding effects. The classification model achieved an AUC of 0.975 and a recall of 0.927. False negative predictions were significantly associated with White patients (RR = 1.208; p = 0.050), those never biopsied (RR = 1.079; p = 0.011), and cases with architectural distortion (RR = 1.037; p < 0.001). Higher breast density significantly increased the risk of false positives, with BI-RADS density C (RR = 1.891; p < 0.001) and D (RR = 2.486; p < 0.001). Race and age were not significant predictors for false positives in multivariate analysis. These findings suggest that deep learning models for mammography may underperform in specific subgroups. The study underscores the need for more precise patient subgroup analysis and emphasizes the importance of considering confounding factors in deep learning model evaluations. These insights can help develop fair and interpretable decision-making models in mammography, ultimately enhancing the performance and equity of CADe and CADx applications.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".