no code implementations • 5 Jan 2024 • M. Emre Sahin, Benjamin C. B. Symons, Pushpak Pati, Fayyaz Minhas, Declan Millar, Maria Gabrani, Jan Lukas Robertus, Stefano Mensa
Quantum machine learning with quantum kernels for classification problems is a growing area of research.
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 • 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.
no code implementations • 9 Jun 2021 • Kevin Thandiackal, Tiziano Portenier, Andrea Giovannini, Maria Gabrani, Orcun Goksel
In this work, we propose Genifer (GENeratIve FEature-driven image Replay), where a generative model is trained to replay images that must induce the same hidden features as real samples when they are passed through the classifier.
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 • 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.
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 • 24 Jun 2020 • Ting-An Yen, Hung-Chun Hsu, Pushpak Pati, Maria Gabrani, Antonio Foncubierta-Rodríguez, Pau-Choo Chung
Deep learning-based methods are gaining traction in digital pathology, with an increasing number of publications and challenges that aim at easing the work of systematically and exhaustively analyzing tissue slides.
no code implementations • 17 Jun 2020 • Pushpak Pati, Antonio Foncubierta-Rodriguez, Orcun Goksel, Maria Gabrani
Our framework significantly improves the detection with small training data and achieves on par or superior performance compared to state-of-the-art methods for using the entire training data.
no code implementations • 5 Dec 2019 • Anca-Nicoleta Ciubotaru, Arnout Devos, Behzad Bozorgtabar, Jean-Philippe Thiran, Maria Gabrani
Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of training data has the biggest impact on performance.
Facial Expression Recognition Facial Expression Recognition (FER) +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).