Search Results for author: Guillaume Jaume

Found 18 papers, 8 papers with code

Artificial Intelligence for Digital and Computational Pathology

no code implementations13 Dec 2023 Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu, Anurag Vaidya, Tiffany R. Miller, Faisal Mahmood

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology.

whole slide images

Towards a Visual-Language Foundation Model for Computational Pathology

no code implementations24 Jul 2023 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, Faisal Mahmood

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts.

Contrastive Learning Image Classification +3

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

1 code implementation13 Apr 2023 Guillaume Jaume, Anurag Vaidya, Richard Chen, Drew Williamson, Paul Liang, Faisal Mahmood

We propose fusing both modalities using a memory-efficient multimodal Transformer that can model interactions between pathway and histology patch tokens.

Survival Prediction whole slide images

Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

no code implementations31 Oct 2022 Imaad Zaffar, Guillaume Jaume, Nasir Rajpoot, Faisal Mahmood

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations.

Data Augmentation Generative Adversarial Network +3

Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images

no code implementations26 Apr 2022 Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew F. K. Williamson, Maria Gabrani, Orcun Goksel

Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.

Multiple Instance Learning whole slide images

HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

2 code implementations MICCAI Workshop COMPAY 2021 Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani

Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relationship.

BIG-bench Machine Learning Translation

Hierarchical Graph Representations in Digital Pathology

4 code implementations22 Feb 2021 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, Gerardo Botti, Jean-Philippe Thiran, Maria Frucci, Orcun Goksel, Maria Gabrani

We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions.

Quantifying Explainers of Graph Neural Networks in Computational Pathology

3 code implementations CVPR 2021 Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta-Rodríguez, Florinda Feroce, Anna Maria Anniciello, Tilman Rau, Jean-Philippe Thiran, Maria Gabrani, Orcun Goksel

However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists.

Towards Explainable Graph Representations in Digital Pathology

no code implementations1 Jul 2020 Guillaume Jaume, Pushpak Pati, Antonio Foncubierta-Rodriguez, Florinda Feroce, Giosue Scognamiglio, Anna Maria Anniciello, Jean-Philippe Thiran, Orcun Goksel, Maria Gabrani

Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics.

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

3 code implementations27 May 2019 Guillaume Jaume, Hazim Kemal Ekenel, Jean-Philippe Thiran

We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms.

Optical Character Recognition Optical Character Recognition (OCR) +1

edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

1 code implementation18 Apr 2019 Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, Maria Gabrani

The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings.

Graph Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.