Interpreting Data from Scanned Tables. - Waleed Farrukh; Antonio Foncubierta-Rodríguez; Anca-Nicoleta Ciubotaru; Guillaume Jaume; Costas Bekas; Orçun Göksel; Maria Gabrani (GREC@ICDAR, 2017)

Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks. - Guillaume Jaume; Behzad Bozorgtabar; Hazim Kemal Ekenel; Jean-Philippe Thiran; Maria Gabrani (CoRR, 2018)

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents. - Guillaume Jaume; Hazim Kemal Ekenel; Jean-Philippe Thiran (OST@ICDAR, 2019)

edGNN: a Simple and Powerful GNN for Directed Labeled Graphs. - Guillaume Jaume; An-phi Nguyen; María Rodríguez Martínez; Jean-Philippe Thiran; Maria Gabrani (CoRR, 2019)

HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification. - Pushpak Pati; Guillaume Jaume; Lauren Alisha Fernandes; Antonio Foncubierta-Rodríguez; Florinda Feroce; Anna Maria Anniciello; Giosue Scognamiglio; Nadia Brancati; Daniel Riccio; Maurizio Di Bonito; Giuseppe De Pietro; Gerardo Botti; et al. (UNSURE/GRAIL@MICCAI, 2020)

Towards Explainable Graph Representations in Digital Pathology. - Guillaume Jaume; Pushpak Pati; Antonio Foncubierta-Rodríguez; Florinda Feroce; Giosue Scognamiglio; Anna Maria Anniciello; Jean-Philippe Thiran; Orcun Goksel; Maria Gabrani (CoRR, 2020)

Histocartography: a pipeline for histology image analysis. - Antonio Foncubierta-Rodríguez; Pushpak Pati; Guillaume Jaume; Maria Gabrani (AMIA, 2021)

HistoCartography: A Toolkit for Graph Analytics in Digital Pathology. - Guillaume Jaume; Pushpak Pati; Valentin Anklin; Antonio Foncubierta; Maria Gabrani (COMPAY@MICCAI, 2021)

Quantifying Explainers of Graph Neural Networks in Computational Pathology. - Guillaume Jaume; Pushpak Pati; Behzad Bozorgtabar; Antonio Foncubierta; Anna Maria Anniciello; Florinda Feroce; Tilman Rau; Jean-Philippe Thiran; Maria Gabrani; Orcun Goksel (CVPR, 2021)

Learning Whole-Slide Segmentation from Inexact and Incomplete Labels Using Tissue Graphs. - Valentin Anklin; Pushpak Pati; Guillaume Jaume; Behzad Bozorgtabar; Antonio Foncubierta-Rodríguez; Jean-Philippe Thiran; Mathilde Sibony; Maria Gabrani; Orcun Goksel (MICCAI (2), 2021)

Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology. - Pushpak Pati; Guillaume Jaume; Antonio Foncubierta; Florinda Feroce; Anna Maria Anniciello; Giosuè Scognamiglio; Nadia Brancati; Maryse Fiche; Estelle Dubruc; Daniel Riccio; Maurizio Di Bonito; Giuseppe De Pietro; et al. (CoRR, 2021)

Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images. - Kevin Thandiackal; Boqi Chen; Pushpak Pati; Guillaume Jaume; Drew F. K. Williamson; Maria Gabrani; Orcun Goksel (ECCV (21), 2022)

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images. - Nadia Brancati; Anna Maria Anniciello; Pushpak Pati; Daniel Riccio; Giosuè Scognamiglio; Guillaume Jaume; Giuseppe De Pietro; Maurizio Di Bonito; Antonio Foncubierta; Gerardo Botti; Maria Gabrani; Florinda Feroce; et al. (Database J. Biol. Databases Curation, 2022)

Hierarchical graph representations in digital pathology. - Pushpak Pati; Guillaume Jaume; Antonio Foncubierta-Rodríguez; Florinda Feroce; Anna Maria Anniciello; Giosue Scognamiglio; Nadia Brancati; Maryse Fiche; Estelle Dubruc; Daniel Riccio; Maurizio Di Bonito; Giuseppe De Pietro; et al. (Medical Image Anal., 2022)

Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images. - Imaad Zaffar; Guillaume Jaume; Nasir M. Rajpoot; Faisal Mahmood 001 (ISBI, 2023)

Towards a Visual-Language Foundation Model for Computational Pathology. - Ming Y. Lu; Bowen Chen; Drew F. K. Williamson; Richard J. Chen; Ivy Liang; Tong Ding; Guillaume Jaume; Igor Odintsov; Andrew Zhang; Long Phi Le; Georg Gerber; Anil V. Parwani; et al. (CoRR, 2023)

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples. - Andrew H. Song; Mane Williams; Drew F. K. Williamson; Guillaume Jaume; Andrew Zhang; Bowen Chen; Robert Serafin; Jonathan T. C. Liu; Alexander S. Baras; Anil V. Parwani; Faisal Mahmood 001 (CoRR, 2023)

A General-Purpose Self-Supervised Model for Computational Pathology. - 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; et al. (CoRR, 2023)

Weakly supervised joint whole-slide segmentation and classification in prostate cancer. - Pushpak Pati; Guillaume Jaume; Zeineb Ayadi; Kevin Thandiackal; Behzad Bozorgtabar; Maria Gabrani; Orcun Goksel (Medical Image Anal., 2023)

Transcriptomics-Guided Slide Representation Learning in Computational Pathology. - Guillaume Jaume; Lukas Oldenburg; Anurag Vaidya; Richard J. Chen; Drew F. K. Williamson; Thomas Peeters; Andrew H. Song; Faisal Mahmood 001 (CVPR, 2024)

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction. - Guillaume Jaume; Anurag Vaidya; Richard J. Chen; Drew F. K. Williamson; Paul Pu Liang; Faisal Mahmood 001 (CVPR, 2024)

Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology. - Andrew H. Song; Richard J. Chen; Tong Ding; Drew F. K. Williamson; Guillaume Jaume; Faisal Mahmood 001 (CVPR, 2024)

Multistain Pretraining for Slide Representation Learning in Pathology. - Guillaume Jaume; Anurag Vaidya; Andrew Zhang; Andrew H. Song; Richard J. Chen; Sharifa Sahai; Dandan Mo; Emilio Madrigal; Long Phi Le; Faisal Mahmood 001 (ECCV (33), 2024)

Multimodal Prototyping for cancer survival prediction. - Andrew H. Song; Richard J. Chen; Guillaume Jaume; Anurag J. Vaidya; Alexander S. Baras; Faisal Mahmood 001 (ICML, 2024)

HEST-1k: A Dataset For Spatial Transcriptomics and Histology Image Analysis. - Guillaume Jaume; Paul Doucet; Andrew H. Song; Ming Yang Lu; Cristina Almagro-Pérez; Sophia J. Wagner; Anurag Vaidya; Richard J. Chen; Drew F. K. Williamson; Ahrong Kim; Faisal Mahmood 001 (NeurIPS, 2024)

Artificial Intelligence for Digital and Computational Pathology. - Andrew H. Song; Guillaume Jaume; Drew F. K. Williamson; Ming Y. Lu; Anurag Vaidya; Tiffany R. Miller; Faisal Mahmood 001 (CoRR, 2024)

Multimodal Whole Slide Foundation Model for Pathology. - Tong Ding; Sophia J. Wagner; Andrew H. Song; Richard J. Chen; Ming Y. Lu; Andrew Zhang; Anurag J. Vaidya; Guillaume Jaume; Muhammad Shaban; Ahrong Kim; Drew F. K. Williamson; Bowen Chen; et al. (CoRR, 2024)

Molecular-driven Foundation Model for Oncologic Pathology. - Anurag Vaidya; Andrew Zhang; Guillaume Jaume; Andrew H. Song; Tong Ding; Sophia J. Wagner; Ming Y. Lu; Paul Doucet; Harry Robertson; Cristina Almagro-Pérez; Richard J. Chen; Dina ElHarouni; et al. (CoRR, 2025)

Accelerating Data Processing and Benchmarking of AI Models for Pathology. - Andrew Zhang; Guillaume Jaume; Anurag Vaidya; Tong Ding; Faisal Mahmood 001 (CoRR, 2025)

AI-driven 3D Spatial Transcriptomics. - Cristina Almagro-Pérez; Andrew H. Song; Luca L. Weishaupt; Ahrong Kim; Guillaume Jaume; Drew F. K. Williamson; Konstantin Hemker; Ming Y. Lu; Kritika Singh; Bowen Chen; Long Phi Le; Alexander S. Baras; et al. (CoRR, 2025)

A Foundation Model for Spatial Proteomics. - Muhammad Shaban; Yuzhou Chang; Huaying Qiu; Yao Yu Yeo; Andrew H. Song; Guillaume Jaume; Yuchen Wang; Luca L. Weishaupt; Tong Ding; Anurag Vaidya; Abdallah Lamane; Daniel Shao; et al. (CoRR, 2025)

Evidence-based diagnostic reasoning with multi-agent copilot for human pathology. - Chengkuan Chen; Luca L. Weishaupt; Drew F. K. Williamson; Richard J. Chen; Tong Ding; Bowen Chen; Anurag Vaidya; Long Phi Le; Guillaume Jaume; Ming Y. Lu; Faisal Mahmood 001 (CoRR, 2025)