H&E stain
PulseAugur coverage of H&E stain — every cluster mentioning H&E stain across labs, papers, and developer communities, ranked by signal.
- used by spatial transcriptomics 90%
- used by gene expression 90%
- developed spatial transcriptomics 70%
- used by Gotit.pub 70%
- uses spatial transcriptomics 70%
- used by Xenium 70%
- used by ScienceCast 70%
- used by CatalyzeX 70%
- used by alphaXiv 70%
- instance of gene expression 70%
- instance of spatial transcriptomics 60%
6 day(s) with sentiment data
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New BRIDGE dataset advances computational pathology with paired biopsy images
Researchers have introduced the BRIDGE dataset, a collection of paired brightfield whole-slide images of prostate core-needle biopsies. This dataset, comprising 2607 images from 869 slides across 169 patients, is design…
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Pathology foundation models predict gene expression from histology images
Researchers have developed a method to predict single-cell spatial gene expression directly from H&E stained histology images. By adapting pathology foundation models, their approach significantly improves prediction ac…
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AI maps demographic data from 1.3M pathology images
Researchers have developed a method to map demographic information from routine clinical histopathology images. By analyzing 1.3 million whole-slide images from over 250,000 patients across 25 organs, they utilized path…
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PathXAI system enhances pathology reporting with AI-driven tissue segmentation
Researchers have developed PathXAI, a novel system designed to improve pathology reporting by integrating tissue segmentation with H&E images and domain knowledge. This system aims to guide diagnostic reasoning and gene…
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New MIST method enhances pathology slide analysis using molecular data
Researchers have developed a new method called Molecularly Informed Staining Transform (MIST) to improve the analysis of pathology slides. MIST acts as a projection layer in multiple instance learning frameworks, using …
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New REST framework predicts gene expression from tissue images
Researchers have developed a new framework called REST (Reliability-Guided Spatial Transcriptomics supervision) to predict gene expression from histopathological HE-stained images. This method uses a Biological Program …
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VGL-stGen: Multimodal model links lung histology and gene expression
Researchers have developed VGL-stGen, a multimodal large language model that integrates lung histology images with spatial transcriptomics data. This model, adapted from a pretrained medical vision-language model, uses …
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New REST framework predicts molecular data from H&E images
Researchers have developed a new framework called REST (Reliability-Guided Spatial Transcriptomics) to predict molecular information from H&E-stained histopathological images. This method utilizes a Biological Program B…
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New AI models predict gene expression from histology images
Researchers have developed new methods for predicting gene expression from histology images, offering a more cost-effective alternative to traditional spatial transcriptomics. One approach, GATE-ST, integrates text desc…
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AI virtual staining study reveals independent metrics for reliable results
Researchers have conducted a systematic study on unsupervised generative models for virtual histological staining, focusing on scaling and uncertainty quantification. They evaluated six image-to-image architectures, inc…
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New benchmark CellPath-Bench evaluates pathology foundation models
Researchers have introduced CellPath-Bench, a new benchmark designed to systematically evaluate the cellular representation capabilities of pathology foundation models (PFMs). This benchmark assesses how well these mode…
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IMPLICITSTAINER uses neural implicit functions for virtual staining
Researchers have developed IMPLICITSTAINER, a novel framework for virtual staining that uses neural implicit functions to translate Hematoxylin and eosin (H&E) images into virtual immunostains. This method offers resolu…
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CytoFormer: Molecularly Supervised Cell Foundation Model for Histopathology
Researchers have developed CytoFormer, a novel foundation model for classifying cells in histopathology images. Unlike previous methods that relied on manual pathologist annotations, CytoFormer uses molecular data from …
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New framework explains gene expression predictions from pathology images
Researchers have developed a novel framework to explain gene expression predictions from H&E stained pathology images using vision transformers. This framework combines relevance propagation with concept discovery to li…
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New AI models translate histology images to spatial transcriptomics data
Two new research papers, Path2ST and PaSTel, introduce advanced methods for translating histological images into spatial transcriptomic data. Path2ST utilizes a hierarchical approach, grounding cross-modal translation i…
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New framework links tissue morphology to gene expression predictions
Researchers have developed a new explainable framework that links transcriptional programs to tissue morphology using vision transformer (ViT) models. This framework combines relevance propagation with concept discovery…
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LoRCA framework enables histology to HiP-CT image translation
Researchers have developed LoRCA (LoRA Cycle Adaptation), a novel framework for translating histology images to Hierarchical Phase-Contrast Tomography (HiP-CT) volumes. This method utilizes a frozen DINOv3 backbone with…
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New VOICE model predicts gene expression from tissue images
Researchers have developed VOICE, a novel foundation model that integrates vision and omics data to predict single-cell gene expression from H&E stained tissue images. This model aligns cell morphology with gene express…
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AI guides spatial proteomics to identify cancer recurrence risk niches
Researchers have developed a novel AI-driven framework to analyze spatial proteomics in triple-negative breast cancer (TNBC). This approach integrates AI-generated recurrence risk heatmaps with mass spectrometry data to…
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New AI method approximates RNA signatures from tissue slides
Researchers have developed VITA (VIrtual Transcriptomic Approximation), a novel deep learning approach that approximates RNA signatures from standard H&E stained tissue slides. This method aims to provide a cost-effecti…