Mitigating Bias in Multi-Label Medical Text Classification: A Cooperative Training Framework with Dynamic Debiasing

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ID: 314144
2026
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Abstract
MOTIVATION: Medical text classification plays a critical role in clinical decision support, automated diagnosis, and biomedical research. However, deep learning models are highly susceptible to dataset-induced biases, such as label bias and keyword bias, which can lead to unreliable predictions and poor generalization in real-world clinical applications. Existing debiasing methods often either overcorrect informative samples or lack interpretability during inference, limiting their effectiveness in multi-label medical text classification tasks. RESULTS: We propose Cooperative Debiasing Network (CoDeNet), a cooperative training framework that mitigates dataset bias through dynamic sample reweighting and interpretable counterfactual inference. The framework consists of a primary classifier and a debias estimator, where the debias estimator quantifies sample-level bias and dynamically regulates the optimization process through an elastic scaling mechanism. In addition, a counterfactual post-processing strategy explicitly isolates label-level and keyword-level biases to improve interpretability. Experiments conducted on the DepressionEMO and BDI-Sen datasets demonstrate that CoDeNet consistently improves classification performance over strong Transformer-based baselines, including BERT and MentalBERT. In particular, CoDeNet achieves improvements of up to +6.57% macro-F1 on BDI-Sen and +2.16% macro-F1 on DepressionEMO, with especially strong gains on low-frequency clinical labels. The results indicate that CoDeNet effectively reduces dataset-induced bias while preserving model robustness and interpretability. AVAILABILITY AND IMPLEMENTATION: The source code and implementation details of CoDeNet will be publicly available on GitHub: https://github.com/66ccff39C5BB/CoDeNet.
Reference Key
openalex_W7161682818 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pengfei Li, Xi Zhang, Xiaoyu Hu, Zexu Lin, Deyu Zhou
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag317
URL
https://doi.org/10.1093/bioinformatics/btag317
Keywords Keywords not found

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