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.

翻译：多示例学习已广泛应用于医学影像诊断，其中包标签已知而包内实例标签未知。传统多示例学习假设每个包中的实例是来自给定分布的独立样本。然而，实例通常存在空间或序列上的顺序性，且邻近实例应具有相似的诊断重要性。为解决这一问题，本研究提出了一种平滑注意力深度多示例学习模型。通过在编码每个实例注意力的潜在函数上引入一阶和二阶约束，实现了平滑性。该方法被应用于头部CT扫描中的颅内出血检测。结果表明，该新型SA-DMIL：(a)在扫描（包）和切片（实例）级别上均优于非平滑注意力MIL；(b)学习了切片间的空间依赖性；(c)在相同的ICH测试集上优于当前最先进的MIL方法。