H&E stain
PulseAugur coverage of H&E stain — every cluster mentioning H&E stain across labs, papers, and developer communities, ranked by signal.
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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…
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New DMCoStain framework improves histopathology stain transfer accuracy
Researchers have developed DMCoStain, a new framework for stain transfer in histopathology that iteratively refines both training data and the model itself. This approach aims to improve the accuracy and interpretabilit…
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New AI framework improves PET image retrieval for cancer heterogeneity
Researchers have developed a novel framework for learning representations from 18F-FDG PET imaging data, specifically designed for content-based retrieval of intra-tumour heterogeneity. This weakly supervised method lev…
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New COAST framework enhances gene expression prediction in spatial transcriptomics
Researchers have developed COAST, a novel framework for predicting gene expression in spatial transcriptomics using histology images. This context-aware differential learning approach leverages both local and global con…
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ContiStain framework improves virtual IHC staining with MoE and relation-preserving distillation
Researchers have developed ContiStain, a novel framework designed to improve the performance of virtual immunohistochemistry (IHC) staining models when dealing with sequentially acquired data. This method utilizes a mix…
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AI framework aids liver cancer diagnosis from histopathology images
Researchers have developed a novel framework for diagnosing liver cancers from histopathology images using semantic segmentation. This approach, which assigns the dominant pixel-level label to determine the image-level …
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DriftST framework infers gene expression from histology images
Researchers have developed DriftST, a novel framework for inferring spatially resolved gene expression from H&E stained histology images. This method addresses limitations of existing approaches by enabling efficient on…
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New method predicts molecular data from H&E slides, bypassing RNA sequencing
Researchers have developed a novel method for predicting molecular information from histopathology images, specifically H&E-stained slides, without the need for costly RNA sequencing. By training a lightweight alignment…
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Vision-Language Models Achieve Zero-Annotation Histopathology Segmentation
Researchers have developed a novel approach using vision-language models (VLMs) to perform foreground segmentation in histopathology images without requiring manual annotations. This method treats tissue-versus-backgrou…
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Deep learning predicts breast cancer subtypes from pathology images
Researchers have developed a new deep learning framework to classify breast cancer subtypes using histopathology images, potentially reducing the need for costly molecular assays. The method employs a multi-objective pa…