Enhancing Fairness in Ultrasound Imaging: Evaluating Adversarial Debiasing Across Diverse Patient Demographics
Bibliographic record
Abstract
This paper explores the use of adversarial debiasing algorithms to mitigate bias in ultrasound imaging datasets, focusing on breast and lung images. The study evaluates fairness metrics like area under the curve (AUC), False Positive Rate (FPR), False Negative Rate (FNR), and demographic parity to assess the impact of debiasing using the recently published MEDFAIR framework. While debiasing improves fairness overall, disparities remain in certain subgroups, such as age in the breast dataset and sex in the lung dataset. The paper also compares artificial intelligence (AI) models (ResNet18, AlexNet, VGG16, MobileNetV2, DenseNet121), revealing differences in susceptibility to bias and effectiveness post-debiasing. These findings underscore the challenges of achieving full fairness in AI-driven medical imaging and highlight the need for continued refinement of debiasing methods to ensure equitable outcomes across diverse patient populations.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".