Search Results for author:
Found 4 papers, 1 papers with code
GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation
In this work, we present GRASP, a novel graph-structured multi-magnification framework for processing WSIs in digital pathology.
Non-Euclidean Spatial Graph Neural Network
Besides, existing spatial network representation learning methods can only consider networks embedded in Euclidean space, and can not well exploit the rich geometric information carried by irregular and non-uniform non-Euclidean space.
VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology
To showcase the potential power of our proposed framework, we applied VOLTA to ovarian and endometrial cancers with very small sample sizes (10-20 samples) and demonstrated that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide novel insights that link histopathology and molecular subtypes of endometrial cancer.
CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation
Importantly, our model is able to stratify the patients into different risk cohorts with statistically different outcomes across two large datasets, a task that was previously achievable only using genomic information.
