8 citations · 19 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…
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments
Gan Gao, Andrew H. Song, Fiona Wang +7
Accurate patient diagnoses based on human tissue biopsies are hindered by current clinical practice, where pathologists assess only a limited number of thin 2D tissue slices sectio…
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…
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples
Andrew H. Song, Mane Williams, Drew F. K. Williamson +8
Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D). However, the standard-of-care in pathologic diagnosis involves selectin…