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When applying multi-instance learning (MIL) to make predictions for bags ofinstances, the prediction accuracy of an instance often depends on not only theinstance itself but also its context in the corresponding bag. From theviewpoint of causal inference, such bag contextual prior works as a confounderand may result in model robustness and interpretability issues. Focusing onthis problem, we propose a novel interventional multi-instance learning (IMIL)framework to achieve deconfounded instance-level prediction. Unlike traditionallikelihood-based strategies, we design an Expectation-Maximization (EM)algorithm based on causal intervention, providing a robust instance selectionin the training phase and suppressing the bias caused by the bag contextualprior. Experiments on pathological image analysis demonstrate that our IMILmethod substantially reduces false positives and outperforms state-of-the-artMIL methods.

Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction