390 citations · 397 across the 4 of their papers we have counts for
4 papers
Domain-Specific Foundation Model Improves AI-Based Analysis of Neuropathology
Ruchika Verma, Shrishtee Kandoi, Robina Afzal +34
Foundation models have transformed computational pathology by providing generalizable representations from large-scale histology datasets. However, existing models are predominantl…
Predict Patient Self-reported Race from Skin Histological Images
Shengjia Chen, Ruchika Verma, Kevin Clare +6
Artificial Intelligence (AI) has demonstrated success in computational pathology (CPath) for disease detection, biomarker classification, and prognosis prediction. However, its pot…
A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models
Gabriele Campanella, Shengjia Chen, Ruchika Verma +10
The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantitie…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…