Mitigating bias in multilabel medical text classification: a cooperative training framework with dynamic debiasing

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 multilabel medical text classification tasks.

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 postprocessing 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.

The source code and implementation details of CoDeNet will be publicly available on GitHub: https://github.com/66ccff39C5BB/CoDeNet.