Publications by Guillaume Jaume

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

Guillaume Jaume, Pushpak Pati, Zeineb Ayadi, Kevin Thandiackal, Seyedbehzad Bozorgtabar, Maria Gabrani

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which a ...

ELSEVIER2023

Hierarchical graph representations in digital pathology

Jean-Philippe Thiran, Guillaume Jaume

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities ...

ELSEVIER2022

Graph Representation Learning in Computational Pathology

Guillaume Jaume

Advances in scanning systems have enabled the digitization of pathology slides into Whole-Slide Images (WSIs), opening up opportunities to develop Computational Pathology (CompPath) methods for computer-aided cancer diagnosis and prognosis. CompPath has be ...

EPFL2022

Quantifying Explainers of Graph Neural Networks in Computational Pathology

Jean-Philippe Thiran, Guillaume Jaume, Seyedbehzad Bozorgtabar

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities ...

IEEE COMPUTER SOC2021

HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification

Jean-Philippe Thiran, Guillaume Jaume

Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have l ...

Springer Nature2020

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

Jean-Philippe Thiran, Guillaume Jaume, Hazim Kemal Ekenel

We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely ...

IEEE2019