FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents G Jaume, HK Ekenel, JP Thiran International Conference on Document Analysis and Recognition Workshops …, 2019 | 304 | 2019 |
Hierarchical Graph Representations in Digital Pathology P Pati*, G Jaume*, A Foncubierta, F Feroce, AM Anniciello, ... Medical Image Analysis, 2021 | 94 | 2021 |
Quantifying explainers of graph neural networks in computational pathology G Jaume*, P Pati*, B Bozorgtabar, A Foncubierta, AM Anniciello, F Feroce, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 84 | 2021 |
Hact-net: A hierarchical cell-to-tissue graph neural network for histopathological image classification P Pati*, G Jaume*, LA Fernandes, A Foncubierta-Rodríguez, F Feroce, ... Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and …, 2020 | 74 | 2020 |
Bracs: A dataset for breast carcinoma subtyping in h&e histology images N Brancati, AM Anniciello, P Pati, D Riccio, G Scognamiglio, G Jaume, ... Database 2022, baac093, 2022 | 59 | 2022 |
HistoCartography: A Toolkit for Graph Analytics in Digital Pathology G Jaume*, P Pati*, V Anklin, A Foncubierta, M Gabrani MICCAI workshop on on Computational Pathology (COMPAY), 2021 | 44 | 2021 |
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs V Anklin*, P Pati*, G Jaume*, B Bozorgtabar, A Foncubierta-Rodríguez, ... MICCAI, 2021 | 41 | 2021 |
Towards explainable graph representations in digital pathology G Jaume*, P Pati*, A Foncubierta-Rodriguez, F Feroce, G Scognamiglio, ... ICML workshop on Computational Biology, 2020 | 40 | 2020 |
Towards a general-purpose foundation model for computational pathology RJ Chen, T Ding, MY Lu, DFK Williamson, G Jaume, AH Song, B Chen, ... Nature Medicine 30 (3), 850-862, 2024 | 29* | 2024 |
A visual-language foundation model for computational pathology MY Lu, B Chen, DFK Williamson, RJ Chen, I Liang, T Ding, G Jaume, ... Nature Medicine 30 (3), 863-874, 2024 | 29* | 2024 |
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images K Thandiackal, B Chen, P Pati, G Jaume, DFK Williamson, M Gabrani, ... ECCV, 2022 | 27 | 2022 |
Artificial intelligence for digital and computational pathology AH Song*, G Jaume*, DFK Williamson, MY Lu, A Vaidya, TR Miller, ... Nature Reviews Bioengineering, 1-20, 2023 | 26 | 2023 |
Extracting structured information from a document containing filled form images AF Rodriguez, G Jaume, M Gabrani US Patent 10,755,039, 2020 | 16 | 2020 |
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction G Jaume, A Vaidya, R Chen, D Williamson, P Liang, F Mahmood CVPR, 2023 | 14 | 2023 |
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer P Pati*, G Jaume*, Z Ayadi, K Thandiackal, B Bozorgtabar, M Gabrani, ... Medical Image Analysis, 2023 | 11 | 2023 |
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs G Jaume*, A Nguyen*, MR Martínez, JP Thiran, M Gabrani ICLR workshop on Relational Representation Learning, 2019 | 9 | 2019 |
Interpreting data from scanned tables W Farrukh, A Foncubierta-Rodriguez, AN Ciubotaru, G Jaume, C Bejas, ... 2017 14th IAPR International Conference on Document Analysis and Recognition …, 2017 | 8 | 2017 |
Integrating context for superior cancer prognosis G Jaume, AH Song, F Mahmood Nature Biomedical Engineering 6 (12), 1323-1325, 2022 | 7 | 2022 |
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images I Zaffar*, G Jaume*, N Rajpoot, F Mahmood ISBI, 2022 | 4 | 2022 |
Hierarchical Cell-to-Tissue graph representations for breast cancer subtyping in digital pathology P Pati, G Jaume, A Foncubierta, F Feroce, AM Anniciello, G Scognamiglio, ... arXiv preprint arXiv:2102.11057, 2021 | 3 | 2021 |