  Multi-instance learning (MIL) is an effective paradigm for whole-slide
pathological images (WSIs) classification to handle the gigapixel resolution
and slide-level label. Prevailing MIL methods primarily focus on improving the
feature extractor and aggregator. However, one deficiency of these methods is
that the bag contextual prior may trick the model into capturing spurious
correlations between bags and labels. This deficiency is a confounder that
limits the performance of existing MIL methods. In this paper, we propose a
novel scheme, Interventional Bag Multi-Instance Learning (IBMIL), to achieve
deconfounded bag-level prediction. Unlike traditional likelihood-based
strategies, the proposed scheme is based on the backdoor adjustment to achieve
the interventional training, thus is capable of suppressing the bias caused by
the bag contextual prior. Note that the principle of IBMIL is orthogonal to
existing bag MIL methods. Therefore, IBMIL is able to bring consistent
performance boosting to existing schemes, achieving new state-of-the-art
performance. Code is available at https://github.com/HHHedo/IBMIL.
