Title: MEDebiaser: A Human-AI Feedback System for Mitigating Bias in Multi-label Medical Image Classification Show Chinese title

Title: MEDebiaser：一种用于减轻多标签医学图像分类中偏见的人机反馈系统

Abstract: Medical images often contain multiple labels with imbalanced distributions and co-occurrence, leading to bias in multi-label medical image classification. Close collaboration between medical professionals and machine learning practitioners has significantly advanced medical image analysis. However, traditional collaboration modes struggle to facilitate effective feedback between physicians and AI models, as integrating medical expertise into the training process via engineers can be time-consuming and labor-intensive. To bridge this gap, we introduce MEDebiaser, an interactive system enabling physicians to directly refine AI models using local explanations. By combining prediction with attention loss functions and employing a customized ranking strategy to alleviate scalability, MEDebiaser allows physicians to mitigate biases without technical expertise, reducing reliance on engineers, and thus enhancing more direct human-AI feedback. Our mechanism and user studies demonstrate that it effectively reduces biases, improves usability, and enhances collaboration efficiency, providing a practical solution for integrating medical expertise into AI-driven healthcare. Show Chinese abstract

Abstract: 医学图像通常包含分布不平衡且共现的多个标签，这会导致多标签医学图像分类中的偏差。 医学专业人士和机器学习实践者的密切合作显著推动了医学图像分析的发展。 然而，传统的协作模式难以促进医生和人工智能模型之间的有效反馈，因为通过工程师将医学专业知识整合到训练过程中既耗时又繁琐。 为了弥补这一差距，我们引入了MEDebiaser，这是一个交互式系统，使医生能够直接使用局部解释来优化人工智能模型。 通过将预测与注意力损失函数相结合，并采用定制的排序策略以缓解可扩展性问题，MEDebiaser使医生能够在无需技术专长的情况下减轻偏差，减少对工程师的依赖，从而增强更直接的人机反馈。 我们的机制和用户研究证明，它能有效减少偏差，提高可用性并增强协作效率，为将医学专业知识整合到人工智能驱动的医疗保健中提供了一个实用的解决方案。