We address the problem of \emph{instance label stability} in multiple
instance learning (MIL) classifiers. These classifiers are trained only on
globally annotated images (bags), but often can provide fine-grained
annotations for image pixels or patches (instances). This is interesting for
computer aided diagnosis (CAD) and other medical image analysis tasks for which
only a coarse labeling is provided. Unfortunately, the instance labels may be
unstable. This means that a slight change in training data could potentially
lead to abnormalities being detected in different parts of the image, which is
undesirable from a CAD point of view. Despite MIL gaining popularity in the CAD
literature, this issue has not yet been addressed. We investigate the stability
of instance labels provided by several MIL classifiers on 5 different datasets,
of which 3 are medical image datasets (breast histopathology, diabetic
retinopathy and computed tomography lung images). We propose an unsupervised
measure to evaluate instance stability, and demonstrate that a
performance-stability trade-off can be made when comparing MIL classifiers.

本文探讨了在多实例学习分类器中的实例标签稳定性问题，提出了一个评估实例稳定性的无监督度量，并演示了在比较 MIL 分类器时可以进行性能 - 稳定性权衡。