18 citations · 42 across the 5 of their papers we have counts for
5 papers
Multistain Pretraining for Slide Representation Learning in Pathology
Guillaume Jaume, Anurag Vaidya, Andrew Zhang +7
Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valua…
Multimodal Prototyping for cancer survival prediction
Andrew H. Song, Richard J. Chen, Guillaume Jaume +3
Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratific…
A General-Purpose Self-Supervised Model for Computational Pathology
Richard J. Chen, Tong Ding, Ming Y. Lu +17
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole…
Towards a Visual-Language Foundation Model for Computational Pathology
Ming Y. Lu, Bowen Chen, Drew F. K. Williamson +10
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of di…
Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks
Richard Chen, Yating Jing, Hunter Jackson
Metastatic presence in lymph nodes is one of the most important prognostic variables of breast cancer. The current diagnostic procedure for manually reviewing sentinel lymph nodes,…