arXiv:2606.28676v2 Announce Type: replace-cross Abstract: Predicting distant metastasis from the digital H & E slides of the primary tumor is a critical yet challenging task in computational patholog...
arXiv:2610.33059v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) differ substantially in architecture, pretraining objective, training data and representation dimensionality, yet...
SpaFactor is a lightweight framework that predicts spatial gene expression from hematoxylin and eosin images by fusing central spot visuals with multiscale neighborhood context. It uses a residual MLP to map tissue microenvironment to low‑dimensional latent gene programs, which are decoded into coordinated multi‑gene predictions. Across five public cohorts, SpaFactor outperforms existing methods, especially for spatially variable genes, and better recovers biologically organized spatial patterns.
The paper introduces the Semantic-Aware Subgraph State Space Model (SASG-SSM) for classifying whole slide images in histopathology. It groups spatially connected patches into semantic subgraphs that preserve internal spatial organization, then uses a graph neural network encoder combined with a Mamba-based state space encoder to integrate local and global contextual information. Experiments on four WSI subtyping datasets show consistent performance gains over state‑of‑the‑art methods, with additional robustness in small‑cohort and few‑shot scenarios.
arXiv:2609.00396v1 Announce Type: new Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
arXiv:2607. 18218v1 Announce Type: cross Abstract: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.
The paper introduces the Semantic-Aware Subgraph State Space Model (SASG-SSM) for classifying whole slide images (WSIs) in histopathology. It groups spatially connected patches into semantic-aware subgraphs that preserve internal spatial organization, then uses a graph neural network encoder combined with a Mamba-based state space encoder to capture both local and global contextual information. Experiments on four WSI subtyping datasets show consistent performance gains over state‑of‑the‑art methods, with additional robustness in small‑cohort and few‑shot scenarios.
arXiv:2609.36429v1 Announce Type: new Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods...
arXiv:2608. 07340v1 Announce Type: cross Abstract: Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through registration.
arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...
arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.
arXiv:2606. 28676v1 Announce Type: cross Abstract: Predicting the risk of distant metastasis from primary tumor tissue histology is a critical yet challenging task in computational pathology.