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Spatial transcriptomics (ST) is essential for understanding diseases anddeveloping novel treatments. It measures gene expression of each fine-grainedarea (i.e., different windows) in the tissue slide with low throughput. Thispaper proposes an Exemplar Guided Network (EGN) to accurately and efficientlypredict gene expression directly from each window of a tissue slide image. Weapply exemplar learning to dynamically boost gene expression prediction fromnearest/similar exemplars of a given tissue slide image window. Our EGNframework composes of three main components: 1) an extractor to structure arepresentation space for unsupervised exemplar retrievals; 2) a visiontransformer (ViT) backbone to progressively extract representations of theinput window; and 3) an Exemplar Bridging (EB) block to adaptively revise theintermediate ViT representations by using the nearest exemplars. Finally, wecomplete the gene expression prediction task with a simple attention-basedprediction block. Experiments on standard benchmark datasets indicate thesuperiority of our approach when comparing with the past state-of-the-art(SOTA) methods.

Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction