Search Results for author:
Found 9 papers, 3 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.
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.
AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images
Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task.
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.
Sparse Multi-Modal Graph Transformer With Shared-Context Processing for Representation Learning of Giga-Pixel Images
Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task.
Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images
In order to leverage the multi-magnification information and early fusion with graph convolutional networks, we handle different embedding spaces at each magnification by introducing the Multi-Scale Relational Graph Convolutional Network (MS-RGCN) as a multiple instance learning method.
A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
Recently, diffusion probabilistic models were introduced to generate high quality images.
CCRL: Contrastive Cell Representation Learning
In this work, we investigated the utility of Self-Supervised Learning (SSL) in cell clustering by proposing the Contrastive Cell Representation Learning (CCRL) model.
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning
The proposed algorithm achieved a mean accuracy of $87. 54\%$ and Cohen's kappa of $0. 8106$ in the slide-level classification of $305$ WSIs; performing better than a standard CNN and pathologists without gynecology-specific training.
