Guillaume Jaume
Guillaume Jaume
PhD student @ IBM Research Zurich - EPFL
Adresse e-mail validée de epfl.ch
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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
502019
Towards explainable graph representations in digital pathology
G Jaume, P Pati, A Foncubierta-Rodriguez, F Feroce, G Scognamiglio, ...
ICML workshop on Computational Biology, 2020
142020
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
122020
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
102021
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
52017
Hierarchical Graph Representations in Digital Pathology
P Pati, G Jaume, A Foncubierta, F Feroce, AM Anniciello, G Scognamiglio, ...
Medical Image Analysis, 2021
32021
RF Bead Pull Measurements of the DQW Cavity
J Guillaume
CERN summary report, 2015
32015
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
22021
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
22021
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 e-prints, arXiv: 2102.11057, 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
Extracting structured information from a document containing filled form images
AF Rodriguez, G Jaume, M Gabrani
US Patent 10,755,039, 2020
12020
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
12019
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
2021
Extracting structured information from a document containing filled form images
AF Rodriguez, G Jaume, M Gabrani
US Patent 11,120,209, 2021
2021
Histocartography: an entity-based workflow for histology image analysis
P Pati, G Jaume, AF Rodriguez, M Gabrani
VIRCHOWS ARCHIV 479 (SUPPL 1), S61-S62, 2021
2021
Extracting structured information from a document containing filled form images
AF Rodriguez, G Jaume, M Gabrani
US Patent App. 17/126,532, 2021
2021
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
2020
Supplementary Material for Quantifying Explainers of Graph Neural Networks in Computational Pathology
G Jaume, P Pati, B Bozorgtabar, A Foncubierta, AM Anniciello, F Feroce, ...
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
P Pati, G Jaume, B Bozorgtabar, A Foncubierta-Rodríguez, JP Thiran, ...
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