  Spatial transcriptomics (ST) is essential for understanding diseases and
developing novel treatments. It measures gene expression of each fine-grained
area (i.e., different windows) in the tissue slide with low throughput. This
paper proposes an Exemplar Guided Network (EGN) to accurately and efficiently
predict gene expression directly from each window of a tissue slide image. We
apply exemplar learning to dynamically boost gene expression prediction from
nearest/similar exemplars of a given tissue slide image window. Our EGN
framework composes of three main components: 1) an extractor to structure a
representation space for unsupervised exemplar retrievals; 2) a vision
transformer (ViT) backbone to progressively extract representations of the
input window; and 3) an Exemplar Bridging (EB) block to adaptively revise the
intermediate ViT representations by using the nearest exemplars. Finally, we
complete the gene expression prediction task with a simple attention-based
prediction block. Experiments on standard benchmark datasets indicate the
superiority of our approach when comparing with the past state-of-the-art
(SOTA) methods.
