BiasPruner: Mitigating bias transfer in continual learning for fair medical image analysis
Bibliographic record
Abstract
Continual Learning (CL) enables neural networks to learn new tasks while retaining previous knowledge. However, most CL methods fail to address bias transfer, where spurious correlations propagate to future tasks or influence past knowledge. This bidirectional bias transfer negatively impacts model performance and fairness, especially in medical imaging, where it can lead to misdiagnoses and unequal treatment. In this work, we show that conventional CL methods amplify these biases, posing risks for diverse patient cohorts. To address this, we propose BiasPruner, a framework that mitigates bias propagation through debiased subnetworks, while preserving sequential learning and avoiding catastrophic forgetting. BiasPruner computes a bias attribution score to identify and prune network units responsible for spurious correlations, creating task-specific subnetworks that learn unbiased representations. As new tasks are learned, the framework integrates non-biased units from previous subnetworks to preserve transferable knowledge and prevent bias transfer. During inference, a task-agnostic gating mechanism selects the optimal subnetwork for robust predictions. We evaluate BiasPruner on medical imaging benchmarks, including skin lesion and chest X-ray classification tasks, where biased data (e.g., spurious skin tone correlations) can exacerbate disparities. Our experiments show that BiasPruner outperforms state-of-the-art CL methods in both accuracy and fairness. Code is available at: BiasPruner.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".