44 citations · 148 across the 12 of their papers we have counts for
15 papers · 1 filter
Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning
Daniel Shao, Joel Runevic, Richard J. Chen +4
Multiple Instance Learning (MIL) is the predominant framework for classifying gigapixel whole-slide images in computational pathology. MIL follows a sequence of 1) extracting patch…
A Foundational Multimodal Vision Language AI Assistant for Human Pathology
Ming Y. Lu, Bowen Chen, Drew F. K. Williamson +8
The field of computational pathology has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders.…
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…
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
Ming Y. Lu, Bowen Chen, Andrew Zhang +6
Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-sh…
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction
Guillaume Jaume, Anurag Vaidya, Richard Chen +3
Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival can improve our understanding of patient prognosis. However, this multimodal task is…