Transcriptomics-guided Slide Representation Learning in Computational Pathology

1 code implementation • 19 May 2024 • Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Thomas Peeters, Andrew H. Song, Faisal Mahmood

Across three independent test datasets consisting of 1, 265 breast WSIs, 1, 946 lung WSIs, and 4, 584 liver WSIs, Tangle shows significantly better few-shot performance compared to supervised and SSL baselines.

Artificial Intelligence for Digital and Computational Pathology

no code implementations • 13 Dec 2023 • Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu, Anurag Vaidya, Tiffany R. Miller, Faisal Mahmood

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology.

whole slide images

A General-Purpose Self-Supervised Model for Computational Pathology

no code implementations • 29 Aug 2023 • Richard J. Chen, Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H. Song, Muhammad Shaban, Mane Williams, Anurag Vaidya, Sharifa Sahai, Lukas Oldenburg, Luca L. Weishaupt, Judy J. Wang, Walt Williams, Long Phi Le, Georg Gerber, Faisal Mahmood

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology.

Code Classification Representation Learning +1

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

2 code implementations • 13 Apr 2023 • Guillaume Jaume, Anurag Vaidya, Richard Chen, Drew Williamson, Paul Liang, Faisal Mahmood

We propose fusing both modalities using a memory-efficient multimodal Transformer that can model interactions between pathway and histology patch tokens.

Survival Prediction whole slide images