FEAST：用于空间转录组学的全连接表达性注意力
FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics
将组织建模为全连接图并引入负感知注意力，可迁移到其他需要建模长程成对交互的视觉任务（如病理图像分析、点云配准）；离网采样策略为从全扫描图像中提取更丰富形态上下文提供了新思路。
原文依据 we propose FEAST (Fully connected Expressive Attention for Spatial Transcriptomics), an attention-based framework that models the tissue as a fully connected graph
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论空间转录组学（ST）提供空间分辨的基因表达，为组织结构和复杂疾病提供关键见解。然而，其高昂的成本限制了广泛应用，导致大量关注从易获得的全切片图像中推断空间基因表达。虽然已提出图神经网络来建模组织区域间的相互作用，但它们依赖预定义的稀疏图，无法考虑可能相互作用的点对，从而在捕捉复杂生物关系方面存在结构限制。为解决此问题，我们提出FEAST（用于空间转录组学的全连接表达性注意力），一个基于注意力的框架，将组织建模为全连接图，从而能够考虑所有成对相互作用。为更好地反映生物相互作用，我们引入负感知注意力，它同时建模兴奋性和抑制性相互作用，捕捉标准注意力常忽略的关键负关系。此外，为缓解标准点图像提取中截断或忽略上下文导致的信息损失，我们引入离网采样策略，从中间区域收集额外图像，使模型能够捕捉更丰富的形态上下文。在公开ST数据集上的实验表明，FEAST在基因表达预测方面超越了最优方法，同时提供了生物学上合理的注意力图，阐明了正负相互作用。我们的代码可在https://github.com/starforTJ/FEAST获取。
英文原文摘要 1626 字符
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 often overlooks. Furthermore, to mitigate the information loss from truncated or ignored context in standard spot image extraction, we introduce an off-grid sampling strategy that gathers additional images from intermediate regions, allowing the model to capture a richer morphological context. Experiments on public ST datasets show that FEAST surpasses state-of-the-art methods in gene expression prediction while providing biologically plausible attention maps that clarify positive and negative interactions. Our code is available at https://github.com/starforTJ/ FEAST.