  Multi-view medical image analysis often depends on the combination of
information from multiple views. However, differences in perspective or other
forms of misalignment can make it difficult to combine views effectively, as
registration is not always possible. Without registration, views can only be
combined at a global feature level, by joining feature vectors after global
pooling. We present a novel cross-view transformer method to transfer
information between unregistered views at the level of spatial feature maps. We
demonstrate this method on multi-view mammography and chest X-ray datasets. On
both datasets, we find that a cross-view transformer that links spatial feature
maps can outperform a baseline model that joins feature vectors after global
pooling.
