arXiv AI
BatMIL: Geometry-Embedding State Space Model for Whole-Slide Image Representation
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arXiv AI
Sep 1
Predicting Metastatic Risk from Primary Cancer Tissue Architecture via Distance-Aware Spatial Modeling
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 AI
Oct 27
MOSAIC: Towards a Universal Latent Space for Computational Pathology Foundation Models
arXiv:2610.33059v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) differ substantially in architecture, pretraining objective, training data and representation dimensionality, yet...
arXiv Machine Learning
Sep 25
SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference
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.
Hugging Face Trending Papers
Sep 3
Semantic-Aware Subgraph State Space Model for WSI Classification in Histopathology
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 Computer Vision
Sep 2
SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling
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 AI
Jul 21
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
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.