PRESTIGE-ST: Patch Resolution and Encoder STrategies for Inference of Gene Expression from Spatial Transcriptomics

ABSTRACT Spatial Transcriptomics (ST) integrates histology with spatially resolved gene expression, offering rich insights into tissue architecture and function. However, its clinical and large-scale deployment is hindered by high costs, technical complexity, and limited accessibility. To address this, computational pathology methods have emerged to predict gene expression directly from histology images, typically framing the task as a multi-output regression problem mapping image patches to gene expression profiles. While several Convolutional Neural Network (CNN) models have been proposed, little is known about how performance is influenced by (a) the number of trainable parameters and (b) the patch size used for prediction. Moreover, existing studies rely primarily on quantitative metrics and overlook biological relevance of predictions. In this study, we systematically evaluated multiple convolution based models (including a Vision Transformer (ViT) model) with different patch sizes on the Xenium based Autoimmune Machine Learning Challenge (AMLC) dataset. We assessed model performance on both globally expressed genes and subsets enriched for immune or disease associated pathways. Our findings reveal that compact CNNs trained on larger patches outperform deeper models, offering superior accuracy in predicting gene expression, especially for biologically important genes. These insights provide practical guidance for designing efficient and biologically meaningful models in the emerging field of image-based gene expression prediction. CCS CONCEPTS Computing methodologies → Computer vision; Neural networks; • Applied computing → Computational genomics; Imaging. Competing Interest Statement The authors have declared no competing interest. Footnotes maninder.23csz0015{at}iitrpr.ac.in amit.23csz0012{at}iitrpr.ac.in mxc2982{at}miami.edu raghvendra.mall{at}ieee.org sukrit.gupta{at}iitrpr.ac.in ACM Reference Format: Maninder Kaur, Amit Kumar, Michele Ceccarelli, Raghvendra Mall, and Sukrit Gupta. 2025. PRESTIGE-ST: Patch Resolution and Encoder STrategies for Inference of Gene Expression from Spatial Transcriptomics. In Proceedings of the 2nd International Workshop on Multimedia Computing for Health and Medicine (MCHM ’25), October 27–28, 2025, Dublin, IrelandProceedings of the 33rd ACM International Conference on Multimedia (MM’25), October 27-31, 2025, Dublin, Ireland. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3728424.3760768 I think we accidentally missed Dr. Raghvendra's name in the PDF. He was added in the system, though. Have submitted the revised PDF.