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The advancement of Spatial Transcriptomics (ST) has facilitated thespatially-aware profiling of gene expressions based on histopathology images.Although ST data offers valuable insights into the micro-environment of tumors,its acquisition cost remains expensive. Therefore, directly predicting the STexpressions from digital pathology images is desired. Current methods usuallyadopt existing regression backbones for this task, which ignore the inherentmulti-scale hierarchical data structure of digital pathology images. To addressthis limit, we propose M2ORT, a many-to-one regression Transformer that canaccommodate the hierarchical structure of the pathology images through adecoupled multi-scale feature extractor. Different from traditional models thatare trained with one-to-one image-label pairs, M2ORT accepts multiple pathologyimages of different magnifications at a time to jointly predict the geneexpressions at their corresponding common ST spot, aiming at learning amany-to-one relationship through training. We have tested M2ORT on three publicST datasets and the experimental results show that M2ORT can achievestate-of-the-art performance with fewer parameters and floating-pointoperations (FLOPs). The code is available at:https://github.com/Dootmaan/M2ORT/.

M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Images