M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics from Digital Pathology Images
This work addresses the high cost of ST data acquisition for researchers in computational pathology and oncology by providing a more efficient prediction method, though it appears incremental as it builds on existing regression approaches with specific architectural improvements.
The paper tackled the problem of predicting expensive Spatial Transcriptomics (ST) gene expressions from digital pathology images by proposing M2OST, a many-to-one regression Transformer that leverages multi-scale image features and inter-spot information, achieving state-of-the-art performance with fewer parameters and FLOPs on three public datasets.
The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-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 ST expressions from digital pathology images is desired. Current methods usually adopt existing regression backbones along with patch-sampling for this task, which ignores the inherent multi-scale information embedded in the pyramidal data structure of digital pathology images, and wastes the inter-spot visual information crucial for accurate gene expression prediction. To address these limitations, we propose M2OST, a many-to-one regression Transformer that can accommodate the hierarchical structure of the pathology images via a decoupled multi-scale feature extractor. Unlike traditional models that are trained with one-to-one image-label pairs, M2OST uses multiple images from different levels of the digital pathology image to jointly predict the gene expressions in their common corresponding spot. Built upon our many-to-one scheme, M2OST can be easily scaled to fit different numbers of inputs, and its network structure inherently incorporates nearby inter-spot features, enhancing regression performance. We have tested M2OST on three public ST datasets and the experimental results show that M2OST can achieve state-of-the-art performance with fewer parameters and floating-point operations (FLOPs).