Histology imaging is an important tool in medical diagnosis and research,
enabling the examination of tissue structure and composition at the microscopic
level. Understanding the underlying molecular mechanisms of tissue architecture
is critical in uncovering disease mechanisms and developing effective
treatments. Gene expression profiling provides insight into the molecular
processes underlying tissue architecture, but the process can be time-consuming
and expensive. In this study, we present BLEEP (Bi-modaL Embedding for
Expression Prediction), a bi-modal embedding framework capable of generating
spatially resolved gene expression profiles of whole-slide Hematoxylin and
eosin (H&E) stained histology images. BLEEP uses a contrastive learning
framework to construct a low-dimensional joint embedding space from a reference
dataset using paired image and expression profiles at micrometer resolution.
With this framework, the gene expression of any query image patch can be
imputed using the expression profiles from the reference dataset. We
demonstrate BLEEP's effectiveness in gene expression prediction by benchmarking
its performance on a human liver tissue dataset captured via the 10x Visium
platform, where it achieves significant improvements over existing methods. Our
results demonstrate the potential of BLEEP to provide insights into the
molecular mechanisms underlying tissue architecture, with important
implications in diagnosis and research of various diseases. The proposed
framework can significantly reduce the time and cost associated with gene
expression profiling, opening up new avenues for high-throughput analysis of
histology images for both research and clinical applications.

该研究提出一种生物模态嵌入框架 Bi-modaL Embedding for Expression Prediction (BLEEP)，可以生成整张组织学图像中的基因表达文件，以此代替传统的时间与金钱代价高昂的基因表达分析方式进行医学诊断和研究。

通过双模式对比学习从 H&E 组织学图像中空间预测基因表达

Spatially Resolved Gene Expression Prediction from H&E Histology Images  via Bi-modal Contrastive Learning

Large-scale electron microscopy (EM) datasets generated using (semi-)
automated microscopes are becoming the standard in EM. Given the vast amounts
of data, manual analysis of all data is not feasible, thus automated analysis
is crucial. The main challenges in automated analysis include the annotation
that is needed to analyse and interpret biomedical images, coupled with
achieving high-throughput. Here, we review the current state-of-the-art of
automated computer techniques and major challenges for the analysis of
structures in cellular EM. The advanced computer vision, deep learning and
software tools that have been developed in the last five years for automatic
biomedical image analysis are discussed with respect to annotation,
segmentation and scalability for EM data. Integration of automatic image
acquisition and analysis will allow for high-throughput analysis of
millimeter-range datasets with nanometer resolution.

本文研究了大规模电子显微镜技术下生物医学图像分析的自动化方法和挑战，并讨论了近五年来在自动化人工智能计算机视觉、深度学习和软件工具方面所取得的进步，探讨了自动化图像获取与分析的整合，以实现毫米级纳米分辨率的高通量分析。