SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image
  Classification
Peijie Qiu Pan Xiao Wenhui Zhu Yalin Wang Aristeidis Sotiras
Multiple Instance Learning (MIL) has been widely used in weakly supervised
whole slide image (WSI) classification. Typical MIL methods include a feature
embedding part that embeds the instances into features via a pre-trained
feature extractor and the MIL aggregator that combines instance embeddings into
predictions. The current focus has been directed toward improving these parts
by refining the feature embeddings through self-supervised pre-training and
modeling the correlations between instances separately. In this paper, we
proposed a sparsely coded MIL (SC-MIL) that addresses those two aspects at the
same time by leveraging sparse dictionary learning. The sparse dictionary
learning captures the similarities of instances by expressing them as a sparse
linear combination of atoms in an over-complete dictionary. In addition,
imposing sparsity help enhance the instance feature embeddings by suppressing
irrelevant instances while retaining the most relevant ones. To make the
conventional sparse coding algorithm compatible with deep learning, we unrolled
it into an SC module by leveraging deep unrolling. The proposed SC module can
be incorporated into any existing MIL framework in a plug-and-play manner with
an acceptable computation cost. The experimental results on multiple datasets
demonstrated that the proposed SC module could substantially boost the
performance of state-of-the-art MIL methods. The codes are available at
\href{https://github.com/sotiraslab/SCMIL.git}{https://github.com/sotiraslab/SCMIL.git}.