Graph Neural Networks in Histopathology: Emerging Trends and Future Directions

18 June 2024

Papers citing "Graph Neural Networks in Histopathology: Emerging Trends and Future Directions"

7 / 7 papers shown

Title
Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians Alessandro Farace di Villaforesta Lucie Charlotte Magister Pietro Barbiero Pietro Lió 25 1 0 04 Dec 2023
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations Qitian Wu Wen-Long Zhao Chenxiao Yang Hengrui Zhang Fan Nie Haitian Jiang Yatao Bian Junchi Yan AI4CE 33 74 0 19 Jun 2023
MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis Weiqin Zhao Shujun Wang Maximus C. F. Yeung Tianye Niu Lequan Yu 34 7 0 21 Feb 2023
Multimodal learning with graphs Yasha Ektefaie George Dasoulas Ayush Noori Maha Farhat Marinka Zitnik 35 82 0 07 Sep 2022
A Study of the Generalizability of Self-Supervised Representations Atharva Tendle Mohammad Rashedul Hasan 62 26 0 19 Sep 2021
Predicting Patient Outcomes with Graph Representation Learning Emma Rocheteau Catherine Tong Petar Velickovic Nicholas D. Lane Pietro Lió 38 38 0 11 Jan 2021
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images Yanning Zhou S. Graham Navid Alemi Koohbanani Muhammad Shaban Pheng-Ann Heng Nasir M. Rajpoot MedIm 27 169 0 03 Sep 2019