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20162023
most citedDeep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary

645 citations · 1.5k across the 36 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

eess.IV2023★ 334 cited

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…

cs.CV2023★ 15 cited

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.…

cs.CV2023★ 8 cited

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…

eess.IV2023★ 4 cited

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…

cs.CV2023★ 18 cited

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…

cs.CV2023★ 10 cited

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…

cs.CV2023★ 10 cited

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…

cs.LG2023★ 3 cited

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…