Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection

Understand

Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown.

• Traditional MIL assumes that instances in each bag are independent samples from a given distribution.
• However, instances are often spatially or sequentially ordered, and one would expect similar diagnostic importance for neighboring instances.
• To address this, in this study, we propose a smooth attention deep MIL (SA-DMIL) model.