arXiv AI
Predicting Metastatic Risk from Primary Cancer Tissue Architecture via Distance-Aware Spatial Modeling
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arXiv AI
Jun 30
Predicting Metastatic Risk from Primary Tissue Architecture via Distance-Aware Spatial Modeling
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.
arXiv Computer Vision
Sep 1
Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction
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...
Hugging Face Trending Papers
Aug 17
Concept-based explanation of gene expression prediction from H&E images
Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine H&E images. However, existing explainability methods for vision transformer...
Hugging Face Trending Papers
5d ago
MIST: Multi-Scale Spatial Context for Gene Expression Prediction from Histology
MIST is a Multi‑context Spatial Transformer that predicts spatial gene expression from H&E images by adding two context pathways to a standard Transformer: a coordinate‑invariant local pathway that attends to spatial k‑nearest‑neighbor spots, and a slide pathway that broadcasts a whole‑slide summary to every spot. In intra‑cohort evaluation across six human organ cohorts, MIST improves mean Pearson correlation by 9.7%–11.6% over the strongest baseline for 10‑, 50‑, and 100‑gene panels, and achieves lower MSE and MAE. It also outperforms baselines in leave‑one‑organ‑out transfer, domain recovery, and reproduces spatial expression patterns of cancer‑marker genes.
arXiv AI
4d ago
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images
arXiv:2610.17603v1 Announce Type: cross Abstract: Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly sup...
arXiv Machine Learning
Aug 26
RACR-MIL: Rank-aware contextual reasoning for weakly supervised grading of squamous cell carcinoma using whole slide images
RACR-MIL is a weakly‑supervised method for grading squamous cell carcinoma (SCC) from whole‑slide images, using an attention‑based multiple‑instance learning framework. It introduces a hybrid WSI graph to capture local tissue context and non‑local phenotypic dependencies, and applies rank‑ordering constraints on attention to prioritize higher‑grade tumor regions, mirroring pathologists’ diagnostic reasoning. The approach achieves state‑of‑the‑art performance, improving SCC grading accuracy by 3–9% over existing methods and up to 10% in tumor localization, and a pilot study showed pathologists reported increased grading efficiency in 60% of cases.