  Traditional image-based survival prediction models rely on discriminative
patch labeling which make those methods not scalable to extend to large
datasets. Recent studies have shown Multiple Instance Learning (MIL) framework
is useful for histopathological images when no annotations are available in
classification task. Different to the current image-based survival models that
limit to key patches or clusters derived from Whole Slide Images (WSIs), we
propose Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by
introducing both siamese MI-FCN and attention-based MIL pooling to efficiently
learn imaging features from the WSI and then aggregate WSI-level information to
patient-level. Attention-based aggregation is more flexible and adaptive than
aggregation techniques in recent survival models. We evaluated our methods on
two large cancer whole slide images datasets and our results suggest that the
proposed approach is more effective and suitable for large datasets and has
better interpretability in locating important patterns and features that
contribute to accurate cancer survival predictions. The proposed framework can
also be used to assess individual patient's risk and thus assisting in
delivering personalized medicine. Codes are available at
https://github.com/uta-smile/DeepAttnMISL_MEDIA.
