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. Smoothness is achieved by the introduction of first and second order constraints on the latent function encoding the attention paid to each instance in a bag. The method is applied to the detection of intracranial hemorrhage (ICH) on head CT scans. The results show that this novel SA-DMIL: (a) achieves better performance than the non-smooth attention MIL at both scan (bag) and slice (instance) levels; (b) learns spatial dependencies between slices; and (c) outperforms current state-of-the-art MIL methods on the same ICH test set.

多例学习在医学成像诊断中得到广泛应用，本研究中提出了一种平滑注意力深度多例学习（SA-DMIL）模型，应用于头部CT扫描的颅内出血检测，结果表明该模型在包和实例级别上均优于非平滑注意力多例学习，并学习了切片之间的空间依赖关系，且在相同的颅内出血测试集上优于当前先进的多例学习方法。

深度多实例学习中的平滑注意力：应用于CT颅内出血检测