Fetching the paper…
Reading the bibliography…
As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparency in clinical decisions.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Looking under the hood: Deep neural network visualization to interpret whole-slide image analysis outcomes for colorectal polyps
Bruno Korbar, Andrea M Olofson, Allen P Miraflor, Catherine M Nicka, Matthew A Suriawinata, Lorenzo Torresani, Arief A Suriawinata, and Saeed Hassanpour · 2017
Earlier work this paper cites.
Sungmin Rhee, Seokjun Seo, and Sun Kim · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Albrecht Stenzinger, Laura Parlow, Jan Budczies, Benjamin Goeppert, et al · 2018
Earlier work this paper cites.
Graph cnn for survival analysis on whole slide pathological images
Ruoyu Li, Jiawen Yao, Xinliang Zhu, Yeqing Li, and Junzhou Huang · 2018
Earlier work this paper cites.
Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, and Nasir Rajpoot · 2019
Earlier work this paper cites.
Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
Earlier work this paper cites.
Layerwise relevance visualization in convolutional text graph classifiers
Robert Schwarzenberg, Marc Hübner, David Harbecke, Christoph Alt, and Leonhard Hennig · 2019
Earlier work this paper cites.
Gnnexplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Earlier work this paper cites.
Encoding histopathological wsis using gnn for scalable diagnostically relevant regions retrieval
Yushan Zheng, Bonan Jiang, Jun Shi, Haopeng Zhang, and Fengying Xie · 2019
Earlier work this paper cites.
Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images
Yanning Zhou, Simon Graham, Navid Alemi Koohbanani, Muhammad Shaban, Pheng-Ann Heng, and Nasir Rajpoot · 2019
Earlier work this paper cites.
Representation learning of histopathology images using graph neural networks
Mohammed Adnan, Shivam Kalra, and Hamid R Tizhoosh · 2020
Earlier work this paper cites.
Histographs: graphs in histopathology
Deepak Anand, Shrey Gadiya, and Amit Sethi · 2020
Cited alongside, same era.
Concept attribution: Explaining cnn decisions to physicians
Mara Graziani, Vincent Andrearczyk, Stéphane Marchand-Maillet, and Henning Müller · 2020
Cited alongside, same era.
Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, Dawei Yin, and Yi Chang · 2020
Cited alongside, same era.
Towards explainable graph representations in digital pathology
Guillaume Jaume, Pushpak Pati, Antonio Foncubierta-Rodriguez, Florinda Feroce, Giosue Scognamiglio, Anna Maria Anniciello, Jean-Philippe Thiran, Orcun Goksel, and Maria Gabrani · 2020
Cited alongside, same era.
Capturing cellular topology in multi-gigapixel pathology images
Wenqi Lu, Simon Graham, Mohsin Bilal, Nasir Rajpoot, and Fayyaz Minhas · 2020
Cited alongside, same era.
Robust counterfactual explanations on graph neural networks
Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, and Yong Zhang · 2021
Closest in time.
Artificial intelligence for the next generation of precision oncology, 2021
Pedro J Ballester and Javier Carmona · 2021
Closest in time.
Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through covid-19 and future crises: the pathlake consortium perspective
Lisa Browning, Richard Colling, Emad Rakha, Nasir Rajpoot, Jens Rittscher, Jacqueline A James, Manuel Salto-Tellez, David RJ Snead, and Clare Verrill · 2021
Closest in time.
Histocartography: A toolkit for graph analytics in digital pathology
Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, and Maria Gabrani · 2021
Closest in time.
Quantifying explainers of graph neural networks in computational pathology
Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta, Anna Maria Anniciello, Florinda Feroce, Tilman Rau, Jean-Philippe Thiran, Maria Gabrani, and Orcun Goksel · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
Cited alongside, same era.
Hact-net: A hierarchical cell-to-tissue graph neural network for histopathological image classification
Pushpak Pati, Guillaume Jaume, Lauren Alisha Fernandes, Antonio Foncubierta-Rodríguez, Florinda Feroce, Anna Maria Anniciello, Giosue Scognamiglio, Nadia Brancati, Daniel Riccio, Maurizio Di Bonito, et al · 2020
Cited alongside, same era.
Interpreting graph neural networks for nlp with differentiable edge masking
Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov · 2020
Cited alongside, same era.
Visualization for histopathology images using graph convolutional neural networks
Mookund Sureka, Abhijeet Patil, Deepak Anand, and Amit Sethi · 2020
Cited alongside, same era.
Weakly supervised prostate tma classification via graph convolutional networks
Jingwen Wang, Richard J Chen, Ming Y Lu, Alexander Baras, and Faisal Mahmood · 2020
Cited alongside, same era.
A theory of usable information under computational constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, and Stefano Ermon · 2020
Cited alongside, same era.
Xgnn: Towards model-level explanations of graph neural networks
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji · 2020
Cited alongside, same era.
Closest in time.
Generative causal explanations for graph neural networks
Wanyu Lin, Hao Lan, and Baochun Li · 2021
Closest in time.
Data-efficient and weakly supervised computational pathology on whole-slide images
Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood · 2021
Closest in time.
Cf-gnnexplainer: Counterfactual explanations for graph neural networks
Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri · 2021
Closest in time.
Self-supervised learning with graph neural networks for region of interest retrieval in histopathology
Yigit Ozen, Selim Aksoy, Kemal Kösemehmetoğlu, Sevgen Önder, and Ayşegül Üner · 2021
Closest in time.
Classification of intestinal gland cell-graphs using graph neural networks
Linda Studer, Jannis Wallau, Heather Dawson, Inti Zlobec, and Andreas Fischer · 2021
Closest in time.
Graph information bottleneck for subgraph recognition
Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He · 2021
Closest in time.
Recognizing predictive substructures with subgraph information bottleneck
Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He · 2021
Closest in time.
On explainability of graph neural networks via subgraph explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
Closest in time.