228 citations · 560 across the 25 of their papers we have counts for
28 papers
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…
Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework
Paul Pu Liang, Yun Cheng, Xiang Fan +9
The recent explosion of interest in multimodal applications has resulted in a wide selection of datasets and methods for representing and integrating information from different mod…
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Richard J. Chen, Chengkuan Chen, Yicong Li +4
Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for lo…
Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling
Iain Carmichael, Andrew H. Song, Richard J. Chen +3
Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling com…
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
Richard J. Chen, Rahul G. Krishnan
Tissue phenotyping is a fundamental task in learning objective characterizations of histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology. However…
Algorithm Fairness in AI for Medicine and Healthcare
Richard J. Chen, Tiffany Y. Chen, Jana Lipkova +5
In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Rece…
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning
Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson +8
The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. Howe…
Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks
Richard J. Chen, Ming Y. Lu, Muhammad Shaban +4
Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Desp…
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
Ming Y. Lu, Dehan Kong, Jana Lipkova +5
Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morpholog…
VR-Caps: A Virtual Environment for Capsule Endoscopy
Kagan Incetan, Ibrahim Omer Celik, Abdulhamid Obeid +8
Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate…
COVID-19 in differential diagnosis of online symptom assessments
Anitha Kannan, Richard Chen, Vignesh Venkataraman +2
The COVID-19 pandemic has magnified an already existing trend of people looking for healthcare solutions online. One class of solutions are symptom checkers, which have become very…
Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images
Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen +3
The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological featu…
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce +8
Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both…
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
Richard J. Chen, Ming Y. Lu, Jingwen Wang +4
Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, mo…
Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
Jingwen Wang, Richard J. Chen, Ming Y. Lu +2
Histology-based grade classification is clinically important for many cancer types in stratifying patients distinct treatment groups. In prostate cancer, the Gleason score is a gra…
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding
Ming Y. Lu, Richard J. Chen, Jingwen Wang +2
Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL). However, given the paucity of l…
SLAM Endoscopy enhanced by adversarial depth prediction
Richard J. Chen, Taylor L. Bobrow, Thomas Athey +2
Medical endoscopy remains a challenging application for simultaneous localization and mapping (SLAM) due to the sparsity of image features and size constraints that prevent direct…
Learning Montezuma's Revenge from a Single Demonstration
Tim Salimans, Richard Chen
We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge. Instead of imitating human demonstrations,…
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
Faisal Mahmood, Daniel Borders, Richard Chen +4
Nuclei segmentation is a fundamental task that is critical for various computational pathology applications including nuclei morphology analysis, cell type classification, and canc…