  Weakly supervised instance labeling using only image-level labels, in lieu of
expensive fine-grained pixel annotations, is crucial in several applications
including medical image analysis. In contrast to conventional instance
segmentation scenarios in computer vision, the problems that we consider are
characterized by a small number of training images and non-local patterns that
lead to the diagnosis. In this paper, we explore the use of multiple instance
learning (MIL) to design an instance label generator under this weakly
supervised setting. Motivated by the observation that an MIL model can handle
bags of varying sizes, we propose to repurpose an MIL model originally trained
for bag-level classification to produce reliable predictions for single
instances, i.e., bags of size $1$. To this end, we introduce a novel
regularization strategy based on virtual adversarial training for improving MIL
training, and subsequently develop a knowledge distillation technique for
repurposing the trained MIL model. Using empirical studies on colon cancer and
breast cancer detection from histopathological images, we show that the
proposed approach produces high-quality instance-level prediction and
significantly outperforms state-of-the MIL methods.
