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

Yunan Wu, Francisco M. Castro-Mac'ias, Pablo Morales-Álvarez, R. Molina, A. Katsaggelos
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引用次数: 1

Abstract

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.
平滑注意力深度多实例学习在CT颅内出血检测中的应用
多实例学习(Multiple Instance Learning, MIL)已广泛应用于医学影像诊断中,其中包装袋标签是已知的,而包装袋内的实例标签是未知的。传统的MIL假设每个包中的实例是来自给定分布的独立样本。然而,实例通常在空间上或顺序上是有序的,人们期望相邻实例具有类似的诊断重要性。为了解决这个问题,在本研究中,我们提出了一个平滑注意深度MIL (sa - dil)模型。平滑是通过在隐函数上引入一阶和二阶约束来实现的,隐函数编码了对包中每个实例的关注。将该方法应用于头部CT扫描中颅内出血的检测。结果表明:(a)在扫描(包)和切片(实例)两个层面上,这种新的SA-DMIL都比非平滑注意mmil具有更好的性能;(b)学习切片之间的空间依赖关系;(c)在相同的ICH测试集上优于当前最先进的MIL方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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