no code implementations • 13 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.
no code implementations • 29 Aug 2023 • Richard J. Chen, Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H. Song, Muhammad Shaban, Mane Williams, Anurag Vaidya, Sharifa Sahai, Lukas Oldenburg, Luca L. Weishaupt, Judy J. Wang, Walt Williams, Long Phi Le, Georg Gerber, Faisal Mahmood
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology.
1 code implementation • 27 Jul 2023 • Andrew H. Song, Mane Williams, Drew F. K. Williamson, Guillaume Jaume, Andrew Zhang, Bowen Chen, Robert Serafin, Jonathan T. C. Liu, Alex Baras, Anil V. Parwani, Faisal Mahmood
Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D).
no code implementations • 24 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.
2 code implementations • 13 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.
no code implementations • 7 Jan 2023 • Pushpak Pati, Guillaume Jaume, Zeineb Ayadi, Kevin Thandiackal, Behzad Bozorgtabar, Maria Gabrani, Orcun Goksel
These pseudo labels are then used to train a node classification head for WSI segmentation.
no code implementations • 31 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.
no code implementations • 26 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.
no code implementations • 8 Nov 2021 • Nadia Brancati, Anna Maria Anniciello, Pushpak Pati, Daniel Riccio, Giosuè Scognamiglio, Guillaume Jaume, Giuseppe De Pietro, Maurizio Di Bonito, Antonio Foncubierta, Gerardo Botti, Maria Gabrani, Florinda Feroce, Maria Frucci
Each WSI, and respective ROIs, are annotated by the consensus of three board-certified pathologists into different lesion categories.
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.
2 code implementations • 4 Mar 2021 • Valentin Anklin, Pushpak Pati, Guillaume Jaume, Behzad Bozorgtabar, Antonio Foncubierta-Rodríguez, Jean-Philippe Thiran, Mathilde Sibony, Maria Gabrani, Orcun Goksel
Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire.
4 code implementations • 22 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.
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.
no code implementations • 1 Jul 2020 • Pushpak Pati, Guillaume Jaume, Lauren Alisha Fernandes, Antonio Foncubierta, Florinda Feroce, Anna Maria Anniciello, Giosue Scognamiglio, Nadia Brancati, Daniel Riccio, Maurizio Do Bonito, Giuseppe De Pietro, Gerardo Botti, Orcun Goksel, Jean-Philippe Thiran, Maria Frucci, Maria Gabrani
Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes.
General Classification Histopathological Image Classification +1
no code implementations • 1 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.
3 code implementations • 27 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
1 code implementation • 18 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.
Ranked #32 on Graph Classification on MUTAG
no code implementations • 9 Nov 2018 • Guillaume Jaume, Behzad Bozorgtabar, Hazim Kemal Ekenel, Jean-Philippe Thiran, Maria Gabrani
We introduce a new scene graph generation method called image-level attentional context modeling (ILAC).