[2603.25247] FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.25247 (cs) [Submitted on 26 Mar 2026] Title:FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics Authors:Taejin Jeong, Joohyeok Kim, Jinyeong Kim, Chanyoung Kim, Seong Jae Hwang View a PDF of the paper titled FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics, by Taejin Jeong and 4 other authors View PDF HTML (experimental) Abstract:Spatial Transcriptomics (ST) provides spatially-resolved gene expression, offering crucial insights into tissue architecture and complex diseases. However, its prohibitive cost limits widespread adoption, leading to significant attention on inferring spatial gene expression from readily available whole slide images. While graph neural networks have been proposed to model interactions between tissue regions, their reliance on pre-defined sparse graphs prevents them from considering potentially interacting spot pairs, resulting in a structural limitation in capturing complex biological relationships. To address this, we propose FEAST (Fully connected Expressive Attention for Spatial Transcriptomics), an attention-based framework that models the tissue as a fully connected graph, enabling the consideration of all pairwise interactions. To better reflect biological interactions, we introduce negative-aware attention, which models both excitatory and inhibitory interactions, capturing essential negative relationships that standard attention ...