Class Unbiasing for Generalization in Medical Diagnosis

• URL: http://arxiv.org/abs/2508.06943v2
• Date: Sun, 31 Aug 2025 12:48:01 GMT
• Title: Class Unbiasing for Generalization in Medical Diagnosis
• Authors: Lishi Zuo, Man-Wai Mak, Lu Yi, Youzhi Tu,
• Abstract summary: We aim to train a class-unbiased model (Cls-unbias) that mitigates both class imbalance and class-feature bias simultaneously.<n>Specifically, we propose a class-wise inequality loss which promotes equal contributions of classification loss from positive-class and negative-class samples.
• Score: 31.19842799888553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical diagnosis might fail due to bias. In this work, we identified class-feature bias, which refers to models' potential reliance on features that are strongly correlated with only a subset of classes, leading to biased performance and poor generalization on other classes. We aim to train a class-unbiased model (Cls-unbias) that mitigates both class imbalance and class-feature bias simultaneously. Specifically, we propose a class-wise inequality loss which promotes equal contributions of classification loss from positive-class and negative-class samples. We propose to optimize a class-wise group distributionally robust optimization objective-a class-weighted training objective that upweights underperforming classes-to enhance the effectiveness of the inequality loss under class imbalance. Through synthetic and real-world datasets, we empirically demonstrate that class-feature bias can negatively impact model performance. Our proposed method effectively mitigates both class-feature bias and class imbalance, thereby improving the model's generalization ability.

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