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Guillaume Jaume
Guillaume Jaume
Harvard Medical School, Brigham and Women's Hospital
Verified email at bwh.harvard.edu
Title
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Cited by
Year
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
802019
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
242020
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
222021
Towards explainable graph representations in digital pathology
G Jaume, P Pati, A Foncubierta-Rodriguez, F Feroce, G Scognamiglio, ...
ICML workshop on Computational Biology, 2020
202020
Hierarchical Graph Representations in Digital Pathology
P Pati, G Jaume, A Foncubierta, F Feroce, AM Anniciello, G Scognamiglio, ...
Medical Image Analysis, 2021
142021
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
92021
Method and system for extracting information from an image of a filled form document
AF Rodriguez, M Gabrani, G Jaume
US Patent 10,769,425, 2020
62020
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
62017
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
52021
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
32021
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
G Jaume, A Nguyen, MR Martínez, JP Thiran, M Gabrani
arXiv preprint arXiv:1904.08745, 2019
32019
Bracs: A dataset for breast carcinoma subtyping in h&e histology images
N Brancati, AM Anniciello, P Pati, D Riccio, G Scognamiglio, G Jaume, ...
arXiv preprint arXiv:2111.04740, 2021
22021
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks
G Jaume, B Bozorgtabar, HK Ekenel, JP Thiran, M Gabrani
arXiv preprint arXiv:1811.03830, 2018
22018
Interpretation of whole-slide images in digital pathology
P Pati, G Jaume, AF Rodriguez, M Gabrani
US Patent App. 16/953,377, 2022
2022
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
K Thandiackal, B Chen, P Pati, G Jaume, DFK Williamson, M Gabrani, ...
arXiv preprint arXiv:2204.12454, 2022
2022
Extracting structured information from a document containing filled form images
AF Rodriguez, G Jaume, M Gabrani
US Patent App. 17/531,912, 2022
2022
Graph Representation Learning and Explainability in Breast Cancer Pathology: Bridging the Gap between AI and Pathology Practice
P Pati, G Jaume, A Foncubierta-Rodriguez, F Feroce, G Scognamiglio, ...
Artificial Intelligence Applications In Human Pathology, 243, 2022
2022
Graph Representation Learning in Computational Pathology
G Jaume
EPFL, 2022
2022
Extracting structured information from a document containing filled form images
AF Rodriguez, G Jaume, M Gabrani
US Patent 11,188,713, 2021
2021
Extracting structured information from a document containing filled form images
AF Rodriguez, G Jaume, M Gabrani
US Patent 11,120,209, 2021
2021
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