Smooth Attention for Deep Multiple Instance Learning: Application to CT
  Intracranial Hemorrhage Detection
Yunan Wu Francisco M. Castro-Macías Pablo Morales-Álvarez Rafael Molina Aggelos K. Katsaggelos
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.