15 citations · 28 across the 4 of their papers we have counts for
4 papers
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…
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective
Shengjia Chen, Gabriele Campanella, Abdulkadir Elmas +8
Recent advances in artificial intelligence (AI), in particular self-supervised learning of foundation models (FMs), are revolutionizing medical imaging and computational pathology…
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling
Gabriele Campanella, Eugene Fluder, Jennifer Zeng +2
Artificial Intelligence (AI) has great potential to improve health outcomes by training systems on vast digitized clinical datasets. Computational Pathology, with its massive amoun…
Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
Gabriele Campanella, Ricky Kwan, Eugene Fluder +10
Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream t…