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

TL;DR

MEDebiaser is an interactive system that enables physicians to directly refine multi-label medical image classification models using local explanations, effectively reducing bias and improving collaboration without requiring technical expertise.

Contribution

It introduces MEDebiaser, a novel human-AI feedback system that simplifies bias mitigation in medical imaging by allowing direct physician input through explanation-based refinement.

Findings

Reduces bias in multi-label medical image classification

Enhances collaboration efficiency between physicians and AI

Improves model usability and trustworthiness

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.