从斑点到像素：基于组织学图像的密集空间基因表达预测
From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images
将空间基因表达预测从基于裁剪斑块的分类/回归范式转变为密集连续图预测，并支持多尺度斑块聚合，为病理图像中的空间组学分析提供了可复用的密集预测框架。
原文依据 we generate a spatially dense continuous gene expression map from the histopathology slide image, and aggregate values within spots of interest to predict the gene expression
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）能够在精细的空间分辨率上测量基因表达，为组织分子景观提供见解。以往的空间基因表达预测方法通常从组织病理学切片图像中裁剪感兴趣斑点，并训练模型将每个斑点映射到相应的基因表达谱。然而，这些方法本质上损失了基因表达的空间分辨率：1）每个斑点通常包含多个具有不同基因表达谱的细胞；2）斑点通常定义在固定的空间分辨率上，限制了在不同尺度上预测基因表达的能力。为解决这些局限，本文提出 PixNet，一种密集预测网络，能够直接从组织病理学切片图像中预测不同大小和尺度斑点的空间分辨基因表达。与以往将单个斑点映射到基因表达值的方法不同，我们从组织病理学切片图像生成空间密集的连续基因表达图，并聚合感兴趣斑点内的值以预测基因表达。我们的 PixNet 在四个常见 ST 数据集上、多个空间尺度下均优于最优方法。源代码见：https://github.com/wangzrk/From-Spots-to-Pixels。
英文原文摘要 1305 字符
Spatial transcriptomics (ST) measures gene expression at fine-grained spatial resolution, offering insights into tissue molecular landscapes. Previous methods for spatial gene expression prediction typically crop spots of interest from histopathology slide images, and train models to map each spot to a corresponding gene expression profile. However, these methods inherently lose the spatial resolution in gene expression:1) each spot often contains multiple cells with distinct gene expression profiles;2) spots are typically defined at fixed spatial resolutions, limiting the ability to predict gene expression at varying scales. To address these limitations, this paper presents PixNet, a dense prediction network capable of predicting spatially resolved gene expression across spots of varying sizes and scales directly from histopathology slide images. Different from previous methods that map individual spots to gene expression values, we generate a spatially dense continuous gene expression map from the histopathology slide image, and aggregate values within spots of interest to predict the gene expression. Our PixNet outperforms state-of-the-art methods on four common ST datasets in multiple spatial scales. The source code is available at: https://github.com/wangzrk/From-Spots-to-Pixels.