Histological
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Related Articles from SNS
Quantifying the Localization of Histological Staining Markers within the GI Epithelial Unit Axis: A Gastrointestinal Spatial Pathology Plugin for ImageJ
Histological analysis is crucial for understanding gastrointestinal (GI) tract homeostasis and disease pathophysiology. Various histological stains are commonly used in research settings for assessing development, disease pathogenesis, and therapeutic impacts. Specifically in the ordered architecture of the GI epithelium, current semi-quantitative analysis of histological staining relies heavily on manual scoring rubrics and often lacks robust spatial assessment.
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
Announce Type: new Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing the need for costly single-cell ST measurements. Unlike existing histology-to-ST methods that mainly predict spot-level profiles for local regions containing multiple cells, this task requires modeling cell-to-cell expression variability, which is strongly structured by cell type. We...
Parameter-Efficient Subspace Decoupling ViT for Mitigating Multi-Task Negative Transfer in Histological Scoring
arXiv:2605.29852v2 Announce Type: replace Abstract: Histological scoring is essential for diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD), yet its automation remains challenging due to the high annotation cost and negative transfer among the strongly correlated NAFLD Activity Score (NAS) indicators in multi-task learning. To address this issue, we propose a subspace-decoupled multi-task Vision Transformer (ViT) that integrates lightweight task-specific Adapters with orthogonality-based...
SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology
Histomorphology and spatial transcriptomics capture complementary aspects of tissue biology, but their relationships remain difficult to extract, align, and interpret at scale. Existing foundation models typically connect histology, omics, or language only pairwise, which limits their capacity to jointly infer molecular states, decode spatial tissue organization, and generate biologically grounded explanations. Here, we show SciCore-Omics, the first tri-modal foundation model linking...
Multivariate integration of histological images and gene expression data: a comparative review
Integrating histological images with gene expression data offers a promising approach for linking tissue morphologies to molecular signatures and improving disease subtyping. However, such integration remains challenging due to the high dimensionality of these datasets, cross-modal heterogeneity, and limited interpretability. Multivariate methods such as Sparse Canonical Correlation Analysis (Sparse CCA), Joint Nonnegative Matrix Factorisation (Joint NMF), and Angle-based Joint and...
Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders
Light microscopy of tissue sections stained with hematoxylin and eosin (H&E) has been the foundation of histopathology for over 150 years and remains essential for diagnosis and research. The development of high-plex spatial profiling approaches able to measure protein and RNA expression at single-cell resolution augments but does not replace H&E imaging, even in research. Computational pathology (CPath) models based on deep learning promise to further increase the value of H&E...
HOPE: Interpretable Histology Analysis with Spatial Omics-Derived Signatures for Precision Oncology
Hematoxylin and eosin (H&E) stained images are fundamental clinical tools for disease assessment. However, even with advanced computational models, their prognostic capabilities remain limited. Spatial omics characterizes tumor microenvironments (TME) in detail yet remains clinically inaccessible due to cost and complexity.
Pathway-Structured Privileged Distillation for Deployable Computational Pathology
Announce Type: new Abstract: Integrating transcriptomics and histopathology can improve cancer risk modelling, yet practical use is constrained by the limited availability of RNA profiling in routine settings. Here we introduce Mixture of Pathway Experts (MoPE), a knowledge-distillation framework that reframes multimodal learning as privileged distillation for histology-only inference. MoPE is motivated by the partial observability between RNA profiles and whole-slide images: histology can...
From unsupervised clustering to atlas-guided annotation in cohort-scale spatial omics with HiCAT
Pathologist-annotated tissue regions provide a fundamental reference for examining spatial omics data, yet such annotations are available for a limited number of samples due to the substantial manual effort required. Moreover, these annotations are derived from morphology within individual histology images, which can overlook molecularly defined regions and obscure intra-sample heterogeneity. To address these limitations, we present HiCAT, a machine-learning framework that automatically...
A Pathology Foundation Model for Gastric Cancer with Real-World Validation
arXiv:2606.04792v1 Announce Type: new Abstract: Gastric cancer remains a major cause of cancer mortality, yet its histological and molecular heterogeneity complicates diagnosis and risk stratification. General-purpose pathology foundation models (PFMs) often plateau on fine-grained endpoints central to gastric cancer care, and few have undergone rigorous prospective validation or clinical reader studies. We present GRACE, a Gastric-specific foundation model for Real-world Assessment and...