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
3d ago
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...
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.
arXiv Computer Vision
2d ago
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 Computer Vision
4d ago
Report Supervision
The paper introduces Report Supervision (R‑Super), a framework that uses radiology reports to supervise tumor segmentation models. By incorporating loss functions that align segmentation outputs with report‑derived tumor counts, sizes, and locations, R‑Super improves detection and segmentation performance. Experiments on kidney and pancreatic tumors show up to a 15% increase in F1‑Score and DSC compared to mask‑only training, outperforming methods like CLIP and multi‑task learning.