645 citations · 1.5k across the 36 of their papers we have counts for
39 papers
Artificial Intelligence for Digital and Computational Pathology
Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson +4
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field…
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…
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…
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…
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
Imaad Zaffar, Guillaume Jaume, Nasir Rajpoot +1
Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelin…
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…
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…
Fast and Scalable Image Search For Histology
Chengkuan Chen, Ming Y. Lu, Drew F. K. Williamson +3
The expanding adoption of digital pathology has enabled the curation of large repositories of histology whole slide images (WSIs), which contain a wealth of information. Similar pa…
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…
Deep Learning-based Frozen Section to FFPE Translation
Kutsev Bengisu Ozyoruk, Sermet Can, Guliz Irem Gokceler +12
Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. The high speed of the procedure allows pathologists to…
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…
Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary
Ming Y. Lu, Melissa Zhao, Maha Shady +4
Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined. This poses a significant challenge since…
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
Kutsev Bengisu Ozyoruk, Guliz Irem Gokceler, Gulfize Coskun +12
Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not sup…
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…